ABSTRACT. The present paper intends to develop a method for evaluating the suitability of urban road networks to accommodate the movement of autonomous buses. The output of this process is the production of a detailed map indicating the score of each road segment in the network. To this end we follow a four-step methodological approach. The first step includes the examination of the current street classification, in order to identify functionality of the road network. The next step deals with investigating specific road network features such as slope, angle degrees in intersections, road surface width (excluding sidewalk) and existence or absence of medians in two-direction roads. After that we map the critical urban spaces or clusters in the study area and calculate their 300m buffer zones, in order to detect clusters with high urban significance. The method is applied to the municipality of Kallithea which is a densely populated suburb in the southern part of Athens, therefore a favourable area for enhancing public transport. The suggested method describes a planning tool that can be utilised by policy makers and planners in order to promote sustainable mobility and the use of public transport in particular, improve travel conditions, enhance social equity and augment smartness. Lastly, the integration of autonomous shuttle buses into the street classification system will facilitate their movement in the complex urban transport systems and will be a driver for shifting from conventional to smart cities.

ABSTRACT. Mobility as a service (MaaS) is a newly emerging concept that is mainly focused on user mobility needs. MaaS is expected to reshape the way people travel and improve future urban mobility. However, research shows that there is neither a consensus on an unequivocal MaaS definition nor a universal best way to evaluate and compare MaaS schemes. Thus, given the large differences between MaaS packages, this research provides new insights into what is offered in a variety of MaaS platforms with special concerns related to functionalities and information types, customisation, and possible integration of specific societal goals. For that purpose, various European MaaS providers/platforms were explored under the PriMaaS partnership, composed of heterogeneous regions. Data were aggregated and analysed applying cross-tabulation and clustering techniques. The proposed approach led to relevant factors, such as Payment, Customisation, and Trip Planning. Relationships between platform characteristics and societal goals suggest these can be used as a conceptual foundation. Our study provides a baseline for establishing a conceptual framework to develop sustainable MaaS indicators, which would help citizens using MaaS more efficiently and support MaaS providers and operators in developing sustainable business models.

ABSTRACT. The cruise shipping industry has become the fastest-growing sector of leisure and tourist business. In terms of the daily cruise service, a key part lies in the accessibility to attractive hinterland destinations. From the viewpoints of safety, efficiency, and environmental protection, public transport is considered to be a good option to access hinterland destinations. It requires the design and selection of public transport routes for cruise passengers by evaluating service quality. The existing studies focus on the evaluation of service quality incorporated with the passenger perspective. However, two pressing challenges require more attention. First, high cruise passenger concentrations, with their specific demands and preferences pressure the public transport system on typical day-trip routes. Second, from a practical view point, given the growth of the cruise shipping industry, the number of cruise passengers is subject to change, which motivates the availability of a flexible evaluation system that allows port cities to re-assess their service quality. Therefore, we set up a four-layer cruise passenger-oriented evaluation system. The selection of the transport route for different cruise passengers in different groups will be used to support the scheduling of public transport and the allocation of public facilities in specific routes within specific time windows. In the first layer, we separate the cruise passengers into different age groups based on their age distribution as we consider that the cruise passengers of different ages have different demands and preferences. In the second layer, we identify the hinterland destinations and use, e.g., the Google Maps platform to obtain the available transport routes from the cruise port to the relevant hinterland destinations. In the third layer, we apply the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) integrated with the Fuzzy Analytic Hierarchy Process (FAHP) to select the best transport route for cruise passengers in each group. In the fourth layer, we explore the influence of the disembarking time of cruise passengers, the public transport timetable, the local information on festivals and road reconstruction, on the selection of the transport route. In doing so, this paper aims at supporting the smart city development for port cities.

ABSTRACT. In this research a new type of urban logistics shipping service which benefits from the already installed passenger transportation network is presented. This new service is based upon the concept of integration of the existing passenger transportation network with the urban freight process, within city centers. The main concern is to reduce the number of fossil combustion powered commercial vehicles traveling within city boundaries, solely for goods transportation, thus contributing to reduce negative effects of urban logistics activities, namely pollution, noise and traffic congestion. In this paper a mathematical model to support the operational planning of this new integrated logistic service is proposed, minimizing the travelled distances by the last-mile operator to deliver the urban goods to the final customer.

ABSTRACT. Scheduling of taxis can reduce cost and potentially decreases CO2 emissions. However, with a rising number of taxis or travel requests, the time for computing schedules increases. A promising alternative is to estimate trip durations based on historical trip data without calculating routes. Based on an analysis of the state of the art, in this paper we identify and investigate two limitations of route-free Estimated Time of Arrival (ETA) models: First, the overall set of features considered by state-of-the-art models is limited. For instance, some potential relevant features (such as weather-related ones) are not considered at all. Also, different models use different sets of features, such as the linear distance between pickup and dropoff location, in diverse and partly inconsistent ways. For those features generally considered, we find different representations, e.g., for trip start time. Second, while discretization of degree-based coordinates for pickup/dropoff locations via spatial binning is very common in state-of-the-art ETA models, the chosen grid cell sizes vary widely and apparently arbitrarily. The contribution of this paper is threefold: First, we propose to enhance route-free ETA models by additional features and investigate the influence of the feature representation on the estimation precision based on a benchmark dataset. Second, we compare different grid cell topologies and sizes as regards their effect on the prediction precision of ETA. Third, we construct and evaluate three types of Machine Learning (ML) models. Our findings indicate that the results outperform state-of-the-art static route-free ETA estimation models.

ABSTRACT. In recent years, an increasing number of shared and on-demand mobility services were deployed in several cities. Most of these services utilize virtual stops as pick-up and drop-off points due to a better service efficiency compared to a door-to-door operating policy. Virtual stops do not include any physical element of a conventional bus stop. Therefore, they are only visible on a mobile device with the proper application. Emerging from the flexibility of distributing virtual stops, strategic considerations are essential to optimize the service quality and usability. This work introduces three approaches to identify locations for virtual stops, followed by methodological strategies. Different characteristics are obtained by applying these methods, assessed by a comprehensive utility analysis regarding users' and providers' perspectives. The evaluation shows that for users, virtual stops located at streetlamps are the most advantageous. In contrast, providers benefit from virtual stops at intersections, mainly due to better routing efficiency.

ABSTRACT. This paper describes the components of an intermodal dispatcher of ride pooling requests to integrate these urban mobility systems with the public transport network in an urban area, thus making possible a new intermodal system. The intermodal dispatcher makes use of a prior dispatcher, developed exclusively for conventional ride pooling systems and a method that filters out the requests that have few possibilities of being served using the public transport system, leaving them to be served directly by ride pooling vehicles. The assignments of customers to vehicles of the ride pooling system are finally determined by an integer programming model of reduced dimensions, so that it can be solved efficiently by conventional solvers.

ABSTRACT. Urban areas around the world are experiencing a sharp growth of shared micro-mobility services mainly because of the introduction of shared dockless bikes and, more recently, of e-scooters. Besides understanding who uses these services and why, more studies are needed to understand when and where these services are used and whether their usage patterns differ. This study aims to expand the current state of knowledge about the usage of micro-mobility services by comparing the spatiotemporal usage patterns of a dockless bike sharing service and an e-scooter service both operating in the city of Turin (Italy). Both visual and statistical approaches are used to analyze and contrast the temporal usage patterns of such services. Usage hotspots are detected by using spatial analysis and contextualized by considering the land use destination. Results indicate that both micro-mobility services are used to perform short trips, which are mainly occurring on weekdays in the afternoon. Usage peaks suggest that both services primarily fulfill the demand for non-commute related travel, in line with previous studies in other countries. Yet, morning usage peaks of dockless BS service shows that the service might be also used for commuting trips to and from university. Usage hotspot detected near to a university district only during weekdays supports this finding. On the other hand, e-scooter trips are mainly concentrated in the city center and in proximity of railway and metro stations, suggesting that, among other purposes, the service is used as first and last-mile connection to public transport.

ABSTRACT. The Lisbon City Hall (Câmara Municipal de Lisboa) developed an app for the citizens to self-report incidents in the city and to supervise its process of resolving the problem. There are many areas that people can report on, however, this article focuses on exploring data related to “sidewalk and accessibility”. The aim of this paper is to elaborate a methodology for simplifying large datasets and guide policymakers in resolving the problems reported. An example of the proposed methodology is used in “sidewalk and accessibility” occurrences to evaluate where and what are the main concerns of citizens regarding this aspect in the city. The methodology proposed in this paper provides insight for local authorities to elaborate urban strategies and policies in favor of resolving the claims and ensuring in this case, a more walkable city.

ABSTRACT. User’s acceptability and perceptions are the important issues for the successful implementation of road pricing schemes. In a developing country like India, the toll collection is switching from Manual Toll Collection (MTC) to the Electronic Toll Collection (ETC) and Open Road Tolling. A better understanding of perceptions and acceptability on different road pricing schemes can help transport planners and decision-makers to know the public support for road pricing schemes before and after implementation. A questionnaire-based survey has been used to explore perceptions and acceptability of road pricing schemes such as High Occupancy Toll (HOT), Dynamic Toll Pricing (DTP), Distance-based pricing etc. For the present study, 550 samples have been taken with the help of an online platform and face-to-face interview. The results show that about 41.18 percent of the users are willing to use the distance-based pricing scheme. The study developed a multinomial logit model to analyze the acceptability of different pricing schemes. The results show that there are significant differences in user acceptability by the type of charging scheme proposed. HOT was most acceptable by users who perceive less travel time savings for using toll roads. The acceptability of DTP decreases for users who felt tolls are not an appropriate mechanism to fund roads. The number of tollbooths passed is a significant parameter affecting the acceptability of distance-based pricing. In addition, socioeconomic variables also affect each of the pricing schemes. The signs of estimated parameters fit prior knowledge and are statically significant. The outcome of the present study will provide a reference for providing a users’ acceptable pricing scheme in India.

ABSTRACT. This paper shows an application of a multi-agent transport simulation to evaluate equity effects of the introduction of a carsharing system. Using vehicles, members and planning data of Oply, a carsharing service that operated in Munich until March 2020, we analyze the evolution of the distribution of costs and benefits among the inhabitants of this city. Introducing the income as an active part of the utility calculation we evaluate how carsharing improves the possibility to better adhere to the agent’s daily schedule. Two scenarios are employed to assess equity in economic terms and accessibility terms. Two different outcomes are expected: firstly, as a high pricing service, carsharing will favor high-income agents, thus skewing benefits towards them; secondly, we show that the granularity of this agent-based simulator makes it a handy tool when conducting policy evaluations on the introduction of a carsharing system.

ABSTRACT. There has been an increasing trend in private vehicle ownership. Despite the flexibility, convenience, and comfort-related advantages of individual transportation, it also represents some negative impacts. This paper proposes a methodology to map the individual transportation traffic-related externalities in an intercity corridor. For that purpose, PTV VISUM is used to develop a transport model. The externalities under study are CO2 and NOx emissions, noise, safety, and congestion. After the estimation of each externality, the information is displayed in a GIS database for analysis. The mapping of such externalities allows to support regional planning policy strategies since it can be applied as an analysis tool that can be used to estimate the impacts of specific scenarios, identify blackspots and provide insights regarding future traffic flow optimization. Using this methodology, it was possible to find the largest blackspot in terms of external costs per VKM (Vehicle-kilometer), road segments that are characterized by high volumes with low road capacity. The findings highlight that the peak-hour period entails 8% higher External Costs per VKM, in particular in the national road, but for the motorway, the value is similar. The total external costs per VKM are 8% higher in the national road during peak hour, while the value is 6% higher for the motorway in the off-peak hour period. Depending on the level of congestion, the weight of each externality differs. For a V/C ratio higher than 1.2, the congestion-related externality weights 80% of the total of externalities, while for a V/C ratio lower than 0.8, the crashes-related externality (80%) is the most prevalent, followed by the CO2-related externality (16%).

ABSTRACT. Road driving behavior plays a key role on vehicular emissions and road safety, but in the case of volatility, which is characterized by hard acceleration/braking, it can lead to both emissions and safety concerns. Although, in general, roundabouts present a better safety performance than other traffic control treatments, different layouts may lead to different severity levels of conflicts. This paper evaluates vehicle movements through three types of roundabouts, aiming to investigate the impact of driving volatility on traffic safety and pollutant emissions based on real-world measurements. Speed was used as a variable parameter directly related to the driver, while vehicular jerk, conflicts as well as global and local (carbon dioxide – CO2) and local (nitrogen oxides – NOx) pollutants were used to evaluate the traffic safety and emissions performance, respectively. Field measurements on several runs obtained from a light-duty probe vehicle equipped with an on-board diagnostic reader while performing three different layout roundabouts, namely, a Single-lane (SL), Compact two-lane (CTL) and Multi-lane (ML) located in suburban environments, were used to develop a microscopic traffic simulation for the baseline and variable speed distributions scenarios. Simulations were conducted using VISSIM, emissions computed using Vehicle Specific Power (VSP), and the Surrogate Safety Assessment Model (SSAM) was applied on trajectory data to recorded for traffic conflicts. The evaluation was based on micro driving behavior analysis impact on the emissions, driving volatility and conflicts for links of the approaching, circulating, and exiting areas of the studied roundabouts. Findings showed that different types of vehicular jerk have a different impact on approaching, circulating and exit area of different roundabouts. In general, vehicular jerk frequency showed its impact on conflicts and pollutant emissions.

ABSTRACT. Although variability of noise and pollutant emissions are usually associated with vehicular speed and acceleration, driving style, mainly related to gear-shift and internal engine variables such as revolutions per minute (RPM) or engine load (EL), can also play a key role. Moreover, the contribution of each variable for fuel consumption, noise and pollutant emissions can vary for different vehicle-motorization types. However, the effect of such internal variables on noise and pollutant emissions is not fully exploited in the literature. Thus, this work aims to assess the impact of the gear selection, RPM, and EL on fuel consumption, and carbon dioxide (CO2), nitrogen oxides (NOx), and noise in terms of sound power level ( Lw) emissions for a diesel passenger vehicle. This is focused on a speed and gear-based controlled on-road environment. Internal observable (fuel consumption, RPM, and EL) and kinematic (speed and acceleration) variables were recorded on a second-by-second time basis using an On-Board Diagnostic System, and noise data were recorded with a Sound Level Meter. Pollutant emissions were estimated using the Vehicle Specific Power (VSP) methodology with a 1Hz frequency. In this study, Clustering and Disjoint Principal Component Analysis is applied to find patterns hidden in data. An Ordered Logit model to predict the gear based on exploring kinetic and internal engine variables, that are influenced by driver’s driving style, is developed. Preliminary results highlight the potential of the developed model and show the potential influence of gear selection for minimizing fuel consumption, noise and pollutant emissions. These findings establish a foundation for developing a sustainability gear-shift indicator that not only is focused on minimizing fuel consumption, but also noise and pollutant emissions. These are relevant for a driver to understand vehicle performance and alleviate the relative impacts of the driving style if integrated into vehicle engine control units.

ABSTRACT. The full decarbonization of the transport sector is one of the the greatest challenges of the coming decades. In this study, both green house gases and local air pollutant emissions are quantified for a full decarbonization scenario and a transition scenario using the Handbook Emission factors for road transport (HBEFA, version 4.1) and the simulation framework MATSim (Multi-Agent Transport simulation). In the full decarbonization scenario, PM10 is found to decrease by 13-15%, PM2.5 by 26-31% and Black Carbon by 63-68% compared to the reference case (year 2020). The applied simulation approach allows for a detailed investigation of further transition scenarios towards full decarbonization.

ABSTRACT. Nonetheless the efforts of the different players, namely oil companies and fuelling stations owners, LPG does not play the role of an alternative to traditional fossil fuels yet. So far, LPG vehicles have failed in being widely accepted by users, namely in Portugal. In fact, the LPG automotive fleet growth has always been modest since its launching on the Portuguese market. Currently, LPG vehicles’ sales are already being surpassed by the "pro-electrification" group formed by hybrids, plug-in hybrids and electric vehicles. In 2019, these latter already represented 1.2% of the Portuguese fleet. Meanwhile, LPG only represented 0.8% (Source: Pordata / INE, 2020). While establishing a parallel with LPG and NG vehicles diffusion and adoption, this paper will explore respondents' opinion, through a survey, about mobility habits and needs, as well as AFVs purchase intention and decision determinants.

ABSTRACT. Modal shift in public transport as a consequence of a disruption on a line has in some cases unforeseen consequences such as an increase in congestion in the rest of the network. How information is provided to users and their behavior plays a central role in such configurations. We introduce here a simple and stylised agent-based model aimed at understanding the impact of behavioral parameters on modal shift. The model is applied on a case study based on a stated preference survey for a segment of Paris suburban train network. We systematically explore the parameter space and show non-trivial patterns of congestion for some values of discrete choice parameters linked to perceived wait time and congestion. Work in progress includes the application of optimization algorithms to the model to search for optimal compromises between congestion in different modes.

ABSTRACT. This paper presents an analysis of the output variability of agent-based transport models. We simulated a MATSim model of the city of Hanover multiple times with identical input and evaluated defined measures of the simulation output on different level of aggregation. Preliminary results show minor variations of values on the global level of aggregation. However, the results show an increased variation when examining the output on the level of districts or for individual agents.

ABSTRACT. We consider the problem of public transport demand estimation. If the public transport demand is known, it is possible to obtain the flow assignment on the different lines assuming that it corresponds to a certain equilibrium and following any of the existing assignment models.

For the practical use of many models of transit assignment in any planning study, the knowledge of the transport demand systematized as an origin-destination matrix is required. Generally, this matrix is very expensive and difficult to obtain. In a previous work we proposed to estimate the od-matrix through the numerical solution of a bilevel optimization problem. One disadvantage of this formulation is the difficulty of obtaining descent directions, therefore, we proposed a derivative-free method for the resolution of the optimization problem. Our goal in this work is to extend the study to larger networks, combining analytical modeling and simulation-optimization methodology.

ABSTRACT. In this paper, the Park-and-Ride (P&R) facilities are considered as short time activities in the daily activity plans of travelers. The purpose of integrating the P&R system into the daily activity plans is its benefits of reducing the undesirable effects of using private vehicles, such as pollution and traffic congestion, in the city center. Previous works did not extensively study the influence of the P&R operational strategy on the travel behavior of workers and shoppers; while this study not only covers this underexamined area of research but focuses on the impact of extending the duration of the P&R facility on traveler mobility. The study is conducted by integrating 13 P&R facilities in Budapest, Hungary into the daily activity plans belonging to the traveler groups of workers and shoppers. The study points out the changes in the travel pattern when P&R is enforced to be used by travelers to hinder travelers from entering the city center or park their cars on street. Besides, a comparison is made with a previously published work to study the impact of changing the P&R activity time and the existing condition where no P&R is provided. The multi-agent transport simulation (MATSim) software is used to conduct the simulation. The results support that using the P&R system increases the total travel time, decreases the number of Vehicle-Miles-Traveled (VMT), and changes the travel pattern. Moreover, the results reveal 5.75 minutes increments in the average trip time, when the P&R duration is increased from 4.5 minutes to 10 minutes. The result led to the conclusion that the operational strategy of the P&R facilities impacts the mobility of travelers, and the P&R system changes the travel pattern, such as the duration of peak periods and the number of vehicles en route.

ABSTRACT. Recent years have been characterized by a strong development of electrical-powered Personal Mobility Vehicles (e-PMVs), i.e., small vehicles equipped with low power electric motors. The most popular among these vehicles is certainly the electric scooter, adopted both as private and shared means of transport. Meanwhile, there has been an increase in the number of accidents that involving this vehicle. This number is expected to grow in the next years due to the mass spread of electric scooter. The laws of some countries, such as Italy, have treated these vehicles equating them to bicycles and authorizing their circulation on cycle paths. However, the different geometrical and mechanical characteristics of electric scooters make this similarity somewhat questionable. For this reason, a study aimed to comparing the dynamic behavior of electric scooters and bicycles is necessary. This research focuses on the vehicular and user vibrational response for different runway pavements conditions and is based on the analysis of acceleration and jerk vectors.

ABSTRACT. This paper assesses how increased use of alternative fuels (ethanol and electricity) contributes to reduce carbon dioxide (CO₂) emissions of light-duty vehicles in Rio Janeiro state (RJ), Brazil, a region of a developing country – most such analysis focus at the aggregate country level. Using an energy systems model (OSeMOSYS), we analyze scenarios that consider an improvement in the quality of the ethanol (in gCO₂eq/MJ) consumed in RJ due to the development of the RenovaBio program, greater uptake of electric vehicles, carbon pricing, and a combination of these policies. We also analyze a scenario with the new RenovaBio targets, which the Brazilian government scaled down because of the COVID-19 pandemic. We find that electric vehicles are the most cost-effective policy to reduce CO₂ emissions (by 1.9%) when no carbon pricing is considered. When it is, however, CO₂ emissions are reduced the most (between 47% and 56%), regardless of the vehicle technology being used, but it is the costlier policy (between 5.4% and 15.3%). In scenarios with carbon pricing, flex-fuel vehicles switch to ethanol, an important result for a region where this technology already dominates the vehicle fleet. Greater uptake of electric vehicles leads to lower overall transportation costs (by 2.5%), but when a CO₂ price is considered, because the electricity system still relies on fossil fuels, the reduction in CO₂ emissions is compromised. In such a setting, increased ethanol quality reduces carbon emissions more than electric vehicle adoption (by 16%). The RenovaBio targets’ reduction due to the COVID-19 pandemic, however, jeopardizes this result. Such findings convey important evidence to policymakers and the research community: decarbonization of transportation in a region of a developing country, where economic resources are scarcer, can start before electric vehicles become affordable by increasing and maintaining continued access to lower-carbon fuels.

ABSTRACT. We employ a state-of-the-art modelling framework to determine the significant factors affecting rates of outdoor exercise trips during Scotland’s COVID-19 lockdown, using secondary data from public surveys conducted by Transport Scotland. The random parameters ordered probit modelling approach is used for its ability to account for the potential effect of unobserved heterogeneity within explanatory variables. The framework is extended further to also allow for detection of heterogeneity among the means of random parameters. We show that various socio-demographic factors (relating mainly to social group, occupation and disability) significantly influenced rates of outdoor exercise during lockdown. Model results are linked to issues surrounding transport equity, as personal vehicle ownership was found to significantly affect rates of outdoor exercise among disabled individuals.

ABSTRACT. The still ongoing COVID-19 pandemic lead to the most significant decrease in global air transport passenger traffic in aviation history. This decrease has been caused by a combination of demand and supply shocks. Main reasons for these shocks were the global travel restrictions, border closures, the rules and regulations for physical distancing as well as income losses of both private companies and private households even though financial support has been provided by many governments. As a consequence, airlines worldwide had to cut down the number of operated routes and flights significantly. Since February 2020, both the number of global passengers and passenger flight kilometers decreased by 80 per cent and more). These developments have caused large financial losses both for airlines and airports worldwide, critically threatening the existence of a large number of these companies. This paper investigates whether and how the economic recovery of the aviation sector should be supported by governments. Our main results indicate that government loans or government secured loans are enabling a well-balanced trade-off between governmental influence, public debt and distortion of competition if the lending criteria are transparent and nondiscriminatory. However, in the course of time, over-indebtedness will become a critical issue for most companies. At this point, non-refundable state grants offer a relatively simple but costly solution since these subsidies will directly increase public debt.

ABSTRACT. This presentation will cover recent innovations in driverless technologies that can better meet the needs of urban living. The transportation system of the future is anticipated to integrate automation and connectivity of vehicles. Enabled by the growing computational power, ubiquity of sensors, big data, and Artificial Intelligence, cities have new opportunities to be more accessible, energy efficient, cleaner, and supportive of more diverse emerging technologies. Automation can improve traffic congestion and lower vehicle operating costs and wireless connectivity can reduce injuries and deaths in collisions. Capturing these opportunities will partly depend on overcoming the infrastructure legacy of the past and addressing the resource challenges of the present. However, through careful systems planning, we can harness new opportunities. The presentation will highlight the promise and the challenges/uncertainties inherent in transitioning to a more technologically advanced system. We will briefly introduce connected and automated vehicle technologies, their status, their prospects, and discuss relevant issues.

ABSTRACT. Motorized Two-wheelers (M2W) and Autorickshaws (M3W) users are vulnerable to frequent road crashes in India, and most of these crashes occur at un-signalized T-intersections. The study aims to model the effect of M2W and M3W conflicts on crossing conflicts. Crossing conflicts are identified using post encroachment time (PET). Traffic video data is collected from 8 urban un-signalized T-intersections in India. The crossing conflicts are characterized as critical conflicts (-1 to 1 s) and non-critical conflicts (≥ 1 and ≤ -1 s) based on PET values. The effect of M2W and M3W on critical and non-critical crossing conflicts is quantified by developing crossing conflict models using Truncated Negative Binomial regression. Results revealed that the proportion of M2W and M3W in the conflicting stream, presence of central traffic island, total conflicting volume significantly influence the number of critical and non-critical conflicts at the unsignalized T-intersections. The study holds a promise in adding useful insights towards modeling conflicts occurring due to small-size motorized vehicles operating under weak-lane mixed traffic conditions.

ABSTRACT. Traffic safety, a forever challenging task is one of the major concerns for researchers and practitioners all over the world. The lack of studies concerning safety in heterogeneous traffic environments with weak lane-based movements motivated this study. Vehicle trajectories have been extracted and used to estimate various safety surrogate measures (SSMs) for identifying the traffic conflicts for safety assessment. Trajectories extracted from the field data of a signalized intersection in India, as well as from various scenarios through microsimulation were analyzed for risk probabilities and compared to reach conclusions about the applicability of an SSM for the prevailing traffic conditions in developing countries like India. The count of conflicting interactions for each scenario also suggests similar results favoring Deceleration Rate to Avoid Collision (DRAC) to be the best-suited SSM for the chosen intersection in this study.

ABSTRACT. An accident Black spot (often synonymously known as a crash hotspot) is a section of road where the frequency of occurrence of several types of road accidents or a particular type of road accident is comparatively higher than other similar sections on the road. Accidents may occur on such sections of a road due to several factors such as faults in engineering design, failures in traffic rule enforcement, rash driving etc. but road accidents repeatedly occur at a location due to faults and inconsistencies in design which lead creation of an ambiguous road environment that fails to provide a positive guidance to road users. For rectification of such road sections, it is important to identify such locations based on likelihood of occurrence of road accidents and past accident history. This paper intends to conduct a critical appraisal of the various methodologies practiced worldwide for the identification of road accident black spots and discusses their merits and demerits. The paper summarizes the key elements in the definitions of road accident Black spots and black road sections of different countries that are a part of protocol of their respective government policies. The paper at last discusses a meta-analysis of the inferences drawn out from these definitions for road accidents.

ABSTRACT. Nowadays, road accident is one of the main causes of mortality worldwide. Then, measures are required to reduce or mitigate the accident impacts. The identification of the most effective measures requires an effective analysis of accidents able to identify and classify the causes that can trigger an accident. The study presented in this paper uses data mining as well as clustering approaches to analyze accident data of the 15 districts of Rome Municipality, collected from 2016 to 2019. Results show that such analyses can be a powerful tool to plan suitable measures to reduce accidents as well as to forecast in advance the areas to be pointed out.

ABSTRACT. Self-driving vehicles, also known as automated vehicles, are expected to be part of daily transportation systems in the next decades. This technology has several advantages, namely improving non-drivers’ mobility and enhancing road safety. Services providing on-demand rides through the use of self-driving vehicles are denominated shared autonomous vehicle (SAV) systems. SAV systems are cheaper to run than traditional on-demand services, since the use of self-driving vehicles lowers driving costs (when compared to ridesourcing), relocation costs (when compared to carsharing), and insurance rates (absence of human error). This has the potential to reduce the service price, and, as a consequence, generate additional demand. It is expected that traditional on-demand services converge into SAV services, and it is highly likely that public transportation will follow. In addition, the market penetration of battery electric vehicles (BEVs) is increasing. Electric powered engines are more efficient than combustion engines, although the use of batteries still has limitations in terms of vehicle range (influenced by battery capacity, charging speeds, driving conditions and weather), and ecological footprint. There are good chances that SAV systems will use BEV technology at the moment self-driving technology becomes available. If this is the case, assuming that charging and battery limitations remain at that time, battery capacity and charging speed will affect the operation of shared autonomous and electric vehicle (SAEV) systems, influencing their planning and operations. Managing the vehicles’ time according to the transportation of clients, charging and relocation needs is a challenge for SAEV upcoming systems. This management is highly dependent on the fleet size, as well as on the number of charging points (sockets) and respective charging speeds.

The design of these main parameters of the SAEV system can be done by two processes: optimization or simulation. In optimization a model is defined for a specific problem by using mathematical equations, while in simulation a system is modeled by defining an environment, the agents, and the interactions between them. Only a few studies deal with the design of SAEV systems. On the optimization side, Zhang et al. (2016) used vehicle routing with model predictive control (MPC) to optimize movements with charging constraints. This approach was later updated by Iacobucci et al. (2019) to incorporate electricity price information for optimizing vehicle charging. On the simulation side, Chen et al. (2016) used an agent-based model to simulate the operations of a fleet of SAEVs and showed that the fleet size is highly dependent on charging infrastructure and vehicle range. This model was later updated by Farhan & Chen (2018) to consider ridesharing with an integrated optimization and discrete event simulation framework. Iacobucci et al. (2018) developed a simulation methodology using a heuristic-based charging strategy to evaluate a SAEV system interacting with demand. All these contributions lack a complete view of the problem parameters (reactive actions in simulation, and rolling horizons in optimization using MPC) leading to near-optimal solutions. Flow-based optimization can be used to produce a tool that allows to obtain global optimal values of the main variables involved in a SAEV system. The flow-based optimization approach already used in the assessment of SAV systems (Liang et al., 2016; Tsao et al., 2018; Iglesias et al., 2018), aggregates vehicle movements into flows reducing the number of decision variables (when compared to vehicle routing).

This work presents an integer programming (IP) model to design a SAEV system’s fleet size, charging facilities, and vehicle operations. It uses profit maximization as an objective and considers vehicle flows in a time-space-energy network (see Fig.1). The aggregation of vehicle movements in flows decreases the computational time, enabling the application of the model to realistic-sized problems (when compared to vehicle routing). The considered approach is possible due to the less detail needed in an early design stage of the system, the planning stage. The model is tested using real mobility data from a region in the center of Portugal.

ABSTRACT. Historically, the aviation industry has been characterized by a clear dichotomy between Low Cost (LCC) and Full Service (FSC) carriers. In recent years, however, airlines have undergone a profound hybridization process, blurring the traditional boundary between these two business models. On the one hand, full service carriers took on some practices of LCCs, such as service unbundling, more competitive pricing strategies and streamlined services, in an effort to become more cost-effective and thus properly cope with the increasing and fierce competition exerted by LCCs, especially on short- and medium-haul routes (Jarach et al., 2009; Bitzan and Peoples, 2016). On the other hand, LCCs have mimicked some archetypal features associated with the FSC business model and focused on new ways to enhancing passenger experience and attracting new market segments; examples include a renewed approach of fare bundling and seat differential pricing (Klophaus et al., 2012), as well as the establishment of operations at primary airports (Dobruszkes et al., 2017). One of the key differences that traditionally distinguished LCCs and FSCs relates to their network structure. While LCCs mostly relied on a pure point-to-point network, FSCs rather used a hub-and-spoke network. In recent years, however, some major LCCs, such as Vueling, Norwegian, Eurowings, and Ryanair, have begun selling connecting itineraries by combining their existing flights and providing integrated ticketing and baggage handling. Consequently, several studies have investigated the rationale behind this initiative, motivated either as part of an integrated feeding strategy with FSC partners (e.g., in the case of Eurowings and Vueling) or to long-haul flights (e.g., Norvegian), or simply to take advantage of the feasible connections (connecting alternatives) that originate from the highly connected and dense network of existing flights (e.g., Ryanair). (Fageda et al., 2015; Fichert and Klophaus, 2016; Maertens et al., 2016; Klophaus and Fichert, 2019; Morlotti et al., 2020). A number of early contributions have focused on quantifying the degree of ”indirect” connectivity and self-hubbing potential provided by the current network of LCCs, founding that these account for more than two-third of the fastest indirect connection in Europe (Malighetti et al., 2008; Maertens et al., 2016; Cattaneo et al., 2017). The more recent works by Klophaus and Fichert (2019) and Morlotti et al. (2020) have instead empirically examined and reported on a given carrier (Ryanair)’s connecting strategy. These latter studies emphasized that Ryanair still mostly relies on its stand-alone nonstop routes, but it has taken first steps to provide connecting services through its large and dense network with the aim to increase passenger volumes and revenues. Yet, its connecting strategy appears immature and experimental: flight schedules are not optimized to facilitate short connecting times on the provided connecting itineraries, and, more importantly, pricing on these itineraries follows a simple additive rule—the price is roughly given by the sum of the two flight legs’ prices plus an additional amount (about 10 Euro). While this avoids the problem of revenue cannibalization, it also denotes minimal (if not totally absent) revenue optimization and exacerbates the challenges related to managing this highly complex and potentially multi-hub situation. Although the presented literature has extensively shown the potential opportunities that may be obtained by leveraging connecting itineraries in LCCs’ networks, these studies provide a rather descriptive than prescriptive approach. By contrast, this work aims to contribute to both literature and practice by developing an integrated optimization framework to assist LCCs’ connection planning. The proposed Integrated Connection Planning and Passenger Allocation Model (ICPPAM) represents itinerary demand based on a discrete choice model, estimated using historical data and considering key itinerary attributes. The derived demand function is explicitly incorporated in the optimization model, thus yielding an explicit treatment of supply-demand interactions. Subject to the current provision of flights, the model aims at maximizing airline’s profits by simultaneously selecting through which airports to enable connections (hub selection) and which specific connections to activate. To illustrate the benefits that can be derived by the application of the ICPPAM, a real-world case study was conducted considering the network of the largest European low-cost carrier, Ryanair, in 2019. Two distinct analyses were conducted to appraise the potential profit increase and business implications of introducing connecting itineraries via a single airport (single-hub scenario) or multiple airports (multi-hub scenario). Additionally, we considered two alternative pricing strategies: an additive pricing approach, which replicates the approach currently used by Ryanair and defines the price of Ryanair’s connecting itineraries as the sum of the prices of the two flight legs, and what we call market-aligned pricing, according to which Ryanair’s itineraries are given an average price that is aligned to the price of itineraries with similar characteristics serving the same market. Through comparison with a baseline network, which reflects the current situation—absent connecting itineraries and consisting of only local nonstop traffic— our preliminary results highlight substantial business opportunities that Ryanair could exploit by optimizing its connection strategy over its existing network of flights. Under a market-aligned pricing strategy, revenue increase is estimated to be as high as 7% (with 10 activated hub airports). On the other hand, much smaller revenue improvements (1.2%) are observed when an additive pricing strategy is used. These improvements follow from two main reasons: first, the capturing of passengers from new markets served through connecting itineraries, and, second, the optimization of passenger mix on each flight. Additionally, these results highlight how the target airline should be able to implement a sophisticated and market-aligned pricing strategy to fully exploit the benefits related to the introduction of connecting flights. This, in practice, requires substantial investment in revenue management and inventory systems. In this respect, the proposed model can be useful to carefully evaluate the adoption and implementation of these systems. At the same time, the endogenization of pricing decisions in the ICPPAM represents with no doubt an interesting (and challenging) avenue for future research efforts.

ABSTRACT. Transit-oriented development, a concept integrating land use and public transport planning, has been widely studied in recent years as a means to reduce car trips and promote sustainable transport modes. However, longitudinal studies on the matter are still rare. Our paper explores Transit-Oriented Development (TOD) effects on travel behavior by analyzing the evolution of the number of car trips after the implementation of a light-rail metro system in the Porto region (Portugal). As Metro do Porto is a large infrastructure project (metro network of 67 km), a macro-analysis at the civil parish level is performed. Changes in the number of car trips are evaluated using a difference-in-differences model, extended to a spatial model to account for metro’s spillover effects. These effects became obvious as metro ridership is reported not only in the directly metro-served parishes, but also in adjacent non-served parishes. Furthermore, we compare the performance of parishes predominantly served with TOD stations to those with transit-adjacent (TAD) stations (located in proximity to urban settlements but not properly articulated with them). We conclude that both station types can reduce the number of car trips, yet only TOD parishes generate significant spillover effects. The importance of other potentially influential factors like building density or socio-economic characteristics, is also discussed.

ABSTRACT. The real-time Railway Traffic Management Problem (rtRTMP) consists of detecting and solving time-overlapping conflicting request done by multiple trains on the same track resources. It typically involves taking retiming, reordering or rerouting train actions in such a way that the propagation of disturbances in the railway network is minimized. The rtRTMP is an NP-Hard problem and finding good strategy to simplifying its solution process is paramount to obtain good quality solutions in a short computation. Solving the Train Routing Selection Problem (TRSP) aims to do so, by limiting the number of routing variables through a pre-processing that selects the most promising routing alternatives among the available ones for each train in order to reduce the size of rtRTMP instances. This work studies the performance of an Ant Colony Optimization (ACO) algorithm for the same problem. An integer linear programming formulation for the TRSP is presented and solved using a commercial software, and it is considered as a benchmark. Computational experiments are performed on two case studies of the French railway infrastructure. ACO and the commercial solver perform comparably only on small instances and both are able to find optimal solutions. However, on larger instances, the ACO algorithm outperforms the commercial software, both in terms of computation time and solution quality.

ABSTRACT. The emergence of the Internet-of-things (IoT) has created opportunities in companies to access real data to support their decision making process. Waste systems are not an exception, and real data availability on waste bins fill levels leads to the so called smart waste collection problem. This work explores this problem and proposes a hybrid metaheuristic to solve it considering workload constraints. This methodology consists of: (i) a look-ahead heuristic to decide the days in which collection is necessary and the bins that need to be collected (must-go) according to the present bin fill levels and future bin fill level predictions; and (ii) a simulated annealing/neighborhood search algorithm to choose the bins that can result in more profitable route(s) if visited and the best route(s) to visit these bins. The proposed hybrid metaheuristic is applied to test instances of different sizes and to a real case study with useful and satisfactory results.

ABSTRACT. Rapid growth of world population and the consequent increased demand for motor vehicles, while making no reformations in transportation infrastructure in urban areas, raise more concerns in terms of pollution and traffic problem, particularly during heavy rainfall. One key parameter that affects traffic during heavy rainfall is drainage, which is a major cause of prolonged and heavy traffic jams arising from inability to timely collect and direct surface runoff. On the other hand, it leads to higher likelihood of road accidents and traffic-related issues that impact urban life. Therefore, changing and developing the existing drainage system can have a great impact on traffic management in rainy conditions. Use of smart drainage systems can be an effective measure to deal with the issue in the short-run. This demands manipulation of existing drainage systems to be smarter and adaptable to heavy rain conditions. Hence, in this study, the Fuzzy Delphi method (FDM) was used to evaluate and rank the impact of smart drainage on traffic parameters from various aspects. SPSS 26.0 software (Cronbach's alpha test) were also used to assess reliability the proposed model. For this purpose, a professional questionnaire was prepared and disseminated between those working in transportation field, mainly including traffic and urban designers. The results show that the greatest effects of smart drainages were on Speed with 0.722 points, Flow with 0.713 points, Peak Hour Factor (PHF) with 0.710 points, and Clearance with 0.701 points, respectively.

Rapid growth of world population and the consequent increased demand for motor vehicles, while making no reformations in transportation infrastructure in urban areas, raise more concerns in terms of pollution and traffic problem, particularly during heavy rainfall. One key parameter that affects traffic during heavy rainfall is drainage, which is a major cause of prolonged and heavy traffic jams arising from inability to timely collect and direct surface runoff. On the other hand, it leads to higher likelihood of road accidents and traffic-related issues that impact urban life. Therefore, changing and developing the existing drainage system can have a great impact on traffic management in rainy conditions. Use of smart drainage systems can be an effective measure to deal with the issue in the short-run. This demands manipulation of existing drainage systems to be smarter and adaptable to heavy rain conditions. Hence, in this study, the Fuzzy Delphi method (FDM) was used to evaluate and rank the impact of smart drainage on traffic parameters from various aspects. SPSS 26.0 software (Cronbach's alpha test) were also used to assess reliability the proposed model. For this purpose, a professional questionnaire was prepared and disseminated between those working in transportation field, mainly including traffic and urban designers. The results show that the greatest effects of smart drainages were on Speed with 0.722 points, Flow with 0.713 points, Peak Hour Factor (PHF) with 0.710 points, and Clearance with 0.701 points, respectively.

ABSTRACT. This paper deals with the defining of mobility packages for users in the Mobility as a Service framework by taking city specific parameters into account. Therefore, a method is elaborated that creates package levels for various transportation modes in cities. The method includes urban characteristics, the aspects of city structure, environmental awareness, modal split, and financial features. Mobility packages are created for public transport, bike-sharing, car-sharing, and taxi. In order to create more diverse mobility packages, the preferences of user groups are considered, and special packages are prepared for travelers. The method is tested with a short survey in Budapest, Hungary. The results show that the workers’ package is similar to the basic package, while in case of students and pensioners, the service level of car-sharing and taxi is lowered based on the revealed usage requirements.

ABSTRACT. Mobility-as-a-Service (MaaS) is a recent concept that is seeing increasing interest across the world. First studies and field trials in the context of developed cities suggest that MaaS can influence people’s mobility behavior and create more sustainable transport systems. However, many findings are not transferable to the Global South context, considering that in terms of transport infrastructure, institutional setups, and citizens’ preferences, most developing cities present significantly different characteristics. Thus, many critical questions remain unanswered, e.g., ‘How to implement MaaS in a developing context?’, ‘What are the main challenges?’, and ‘Who should lead this development?’. This research work considers a public-pushed development and aims to shed light on barriers that transport authorities might face. First, barriers are identified through a literature review at the intersection of transport research and public sector innovation. Second, the barriers are analyzed based on the technology, organization, and environment (TOE) framework. Third, relevance for the Global South is determined through a two-round expert survey. Data related issues (e.g., the need for standardized open data) have been identified as the most critical barrier for MaaS implementation. Also, multimodal transport planning and coordinating intermodal trips seem to be crucial challenges, considering the highly fragmented operator landscape and the lack of integrated transport planning approaches in many developing cities. In addition, auto-centric developments, current institutional setups, and transport authorities’ organizational structures could hamper the transition towards MaaS. The research results contribute to the emerging literature on MaaS governance and provide insights on the capabilities of developing cities to establish MaaS and other transport innovations.

ABSTRACT. Mobility-as-a-service (Maas) is a relatively recent concept in transportation. MaaS provides a bundle of mobility services under one plan subscription, allowing its customers to travel on a seamless multimodal system. Many studies have been employed using different research approaches to identify which transport modes are more suitable to be included in a MaaS plan. Results are divergent due to the fact that different mobility services have been considered and also users are not familiar with characteristics that such new service provides. This study aims to identify potential MaaS members characteristics applying an agent-based modelling approach. We simulate a hypothetic MaaS pilot in the city of Berlin, considering potential customers who can evaluate an increase of their daily utility by getting accessibility to a basic MaaS plan characterized by a daily subscription fee payment. Results generally show how MaaS substitutes mainly walking and cycling and it does not affect car use significantly. Moreover, potential customers are primarily former public transport customers and MaaS attracts new car sharing customers who are willing to employ the services longer. Results suggest that under the scenario developed MaaS will not contribute to reduce car ownership.

ABSTRACT. The concept of MaaS has been described as a new way of managing urban and regional Mobility and facilitating the reduction of dependence on the individual vehicle. MaaS systems have been characterized through a single indicator so far, just providing information about the services available to the users or the level of technology used. However, to facilitate the comparison between various platforms by the potential users and/or to facilitate the discussion between stakeholders, it is essential to provide a broader set of measures to evaluate each platform. In this work, we propose a multidimensional system based on three dimensions (coverage, functionality, and sustainability) based on pairs of indicators (geographical coverage and multimodality level; services and technology; contribution to social cohesion and the environment). The analysis of a dozen of mobility services in several European countries confirmed a notion that the MaaS are still in the preliminary development phase and there is still a considerable margin of progress in view of the effective penetration in the market of large-scale systems, with advanced integration levels and effective demonstration of societal impacts.