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In this era of increasing demand for mobility and rapid urban growth, there is a pressing need for a public transit system that is safe, fast, reliable, well-connected, and sustainable. Furthermore, it is essential to reduce the external costs associated with urban transportation, including environmental pollution, noise, congestion, and accidents, to foster sustainable cities. Choosing the right urban transportation system can meet this goal, but it is not an accessible business for decision-makers in the face of several conflicting criteria and ambiguities in the evaluation process. To cope with this, the current paper suggests a multi-criteria group decision-making (MCGDM) framework consisting of fuzzy BWM (Best–Worst method) and fuzzy MAIRCIA (Multi-Attribute Ideal-Real Comparative Analysis) techniques. This extended MCGDM approach has been applied to evaluate six urban transport systems, namely, Trams, Light Rail Trams, Metro (Subway), Bus Rapid Transport, Commuter Trains, and Public Buses based on 11 selection criteria which we have determined after consultation with highly experienced professionals. The fuzzy BWM technique is employed to identify the weights of the criteria. The fuzzy MAIRCA technique is utilized for ranking the alternatives using the calculated weights of the criteria. The proposed approach's validation has been examined with an extensive robustness check. The study is conducted from a general perspective, i.e., not restricted to a particular city. However, with the identified selection criteria, the proposed decision-making procedure can be repeated for a specific city considering any specific requirements, constraints, or limitations of that city. [ABSTRACT FROM AUTHOR]

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing ones internet of things, cloud computing, system architecture, public transport systems, scalability. [ABSTRACT FROM AUTHOR]

As the world becomes more urbanized, there is a strong need for urban public transport to provide sustainable alternative solutions against private-vehicle usage. However, the opportunities for seamless journeys through public transport are still limited and the need for properly designed and operated transport interchanges is vital. The present paper investigates the perceptions and the users' level of satisfaction when using the New Railway Station of Thessaloniki in Greece and the Riga International Coach Terminal in Latvia, in terms of services provision and station's operation. In total, 36 indicators were tested, grouped in eight quality factors, namely travel information, wayfinding information, time and movement, access, comfort and convenience, station attractiveness, safety and security and emergency situation handling. Attitudinal surveys were implemented to determine key performance factors that affect travelers' satisfaction when using the two terminals. Data were collected through on-line questionnaires and were elaborated through descriptive and inferential statistics, including Mann-Whitney two-sample U-testing to assess differences between the samples in variables measured on a 5-point Likert scale, Spearman bivariate correlations to measure the strength of association between the quality indicators and multiple regression analyses to examine the effect of selected attributes on the general satisfaction level of travelers. Results showed that both interchanges perform better in physical quality attributes, like access, travel and wayfinding information provision, but they do not satisfy users' aesthetics expectations in the internal and external area of the interchanges and the surrounding area and they do not cover adequately their feeling of security and safety in the transfer or waiting areas. These results highlighted the users' preferences and concerns which contribute into a satisfactory overall design of the interchanges. In a nutshell, transport interchange design should satisfy both providing a hub for seamless mobility, but also integrating the station as a part of the public realm.

In this paper we explore user preferences with respect to autonomous shuttle buses when these operate as a first/last mile feeder mode to the main public transport system. We analyse the extent to which improvements of the first- and last mile public transport, through the use of autonomous door-to-door shuttle services, can be expected to affect the choice of mode for the entire transport chain in comparison with choice of mode for the first- and last mile trip. The campus of the Technical University of Denmark (DTU) was used as a case study site, and pivoted stated choice experiments were developed as part of a real-life experiment where several autonomous buses were operated at the DTU campus. In the experiment, choice tasks were directed to users that specifically used the shuttle service. Our findings indicate that while AV shuttles did indeed attract some existing public transport users, almost no effect on the overall mode shares was observed. This even holds for scenarios where speed and frequency of the buses were improved considerably compared to those of the experimental setup. The paper thereby underlines that while new autonomous driving technologies have the potential to improve first and last mile services for public transport, they will have a limited effect on the overall choice of mode for the entire trip chain in their current form.

The continuing increase of the on-road private cars is contributing to a deterioration of the urban traffic system. Public transportation is widely used to tackle this issue due to its large ridership. In this paper, we propose a multi-bus dispatching strategy combined with the boarding and holding control (MBDBH) to improve bus utilization and further decrease the passenger excess delay. Dispatching adjustments and operation control are taken into account in the system. At the dispatching level, on the one hand, either a bus platoon or a single bus can be dispatched for each trip to provide adaptive bus capacity to match the highly-fluctuated stop demands, on the other hand, we adjust the bus dispatching time based on the existing timetable to minimize passenger excess waiting time to a large extent. Meanwhile, the operation level incorporates both holding strategy and boarding limit strategy to bring more flexible adjustments in improving bus service. Besides the efficiency, we also minimize the headway variation in order to maintain a high system reliability. The problem is formulated as a Mixed Integer Nonlinear Programming (MINP) problem, which is solved by the commercial solver Gurobi. With the computational complexity as a concern, we propose a distributed algorithm to implement dual decomposition based on the partial Lagrangian relaxation. Finally, numerical examples are investigated to illustrate the significant time reduction of distributed algorithm and the efficiency of our proposed strategy: The proposed MBDBH model can reduce roughly 50% and 30% of remaining passenger volumes when compared with the timetable-based fixed schedule and the optimized single-bus dispatching schedule, respectively. [ABSTRACT FROM AUTHOR]

Forecasting short-term ridership of different origin-destination pairs (i.e., OD matrix) is crucial to the real-time operation of a metro system. However, this problem is notoriously difficult due to the large-scale, high-dimensional, noisy, and highly skewed nature of OD matrices. In this paper, we address the short-term OD matrix forecasting problem by estimating a low-rank high-order vector autoregression (VAR) model. We reconstruct this problem as a data-driven reduced-order regression model and estimate it using dynamic mode decomposition (DMD). The VAR coefficients estimated by DMD are the best-fit (in terms of Frobenius norm) linear operator for the rank-reduced full-size data. To address the practical issue that metro OD matrices cannot be observed in real time, we use the boarding demand to replace the unavailable OD matrices. Moreover, we consider the time-evolving feature of metro systems and improve the forecast by exponentially reducing the weights for historical data. A tailored online update algorithm is then developed for the high-order weighted DMD model (HW-DMD) to update the model coefficients at a daily level, without storing historical data or retraining. Experiments on data from two large-scale metro systems show that the proposed HW-DMD is robust to noisy and sparse data, and significantly outperforms baseline models in forecasting both OD matrices and boarding flow. The online update algorithm also shows consistent accuracy over a long time, allowing us to maintain an HW-DMD model at much low costs. [ABSTRACT FROM AUTHOR]

A multi-bus dispatching strategy is proposed for a ring-shaped road bus transport system, which allows dispatching single bus or multiple buses and incorporates volume dynamics on both buses and stations. Also, the passengers’ perceived waiting time is firstly formulated as one part of the cost function to take passengers’ anxiety into account, and thereby improving the bus quality of service of bus operations. At upstream stations, as many passengers as possible will board the bus, which leads to the less space remaining on the bus and thus the enlongated wait for passengers at downstream stations. With the aim to avoid such phenomenon, the bus boarding control is implemented in the passengers’ boarding process captured by a simultaneous loading model to provide boarding opportunities for the waiting passengers at downstream bus stations. The formulated problem is tackled in two different scenarios, i.e., either with a linear cost or with a nonlinear cost. The linear cost, incorporating the passengers’ actual waiting time and the bus utilization, is firstly converted into a Mixed Integer Linear Programming (MILP) problem, and is solved by the commercial solver Gurobi. With the computational complexity as a concern, two different evolutionary algorithms, Genetic Algorithm (GA) and Harmony Search algorithm (HS), are also adopted to solve the problem in real time. In Scenario 2, the nonlinear cost, integrating the passengers’ perceived waiting time and the bus utilization, is directly solved by both GA and HS. Finally, case studies are provided to illustrate the efficiency of our proposed strategy by comparing with the traditional bus schedule strategies, as well as analyzing the different impacts of the bus loading process when either passengers’ actual waiting time or passengers’ perceived waiting time are taken into account. [ABSTRACT FROM AUTHOR]

Recent studies have shown that public transport users can be exposed to high levels of pollution emitted from their own vehicles and nearby sources. The purpose of this research is to determine the personal exposure of passengers to PM2.5 inside the vehicles of the massive public transport of two of the main and more populated cities of Colombia, Bogotá and Medellín. TM (TransMilenio powered by diesel) and SITVA (electric and gas natural vehicles) were the systems studied. Were evaluated the integration of new vehicles with technologies Euro V and Euro VI in the TM system, the impact of the weekend effect on personal exposure into public transport (TM and SITVA), and the possible differences between personal exposure regarding the ways of the systems (mixed lane or exclusive lane for TM and SITVA). To measure PM2.5 levels, a DustTrak monitor previously calibrated was used. This measurement campaigns lasted for more than 80 hours and a mean of 17000 data of PM2.5 concentrations were obtained for each route. The personal dose was calculated based on the recorded data. The mean PM2.5 concentrations and personal dose found in the research for TM are 167 μg/m³ and 2.3 μg/min, respectively, while, for SITVA they are 41 μg/m³ and 0.53 μg/min, respectively. Therefore, SITVA users have a 5 times lower personal exposure to PM2.5 than TM users. It was also found that due to the poor proportion of new TM vehicles during the monitoring period, the personal exposure in the old vehicles and in the new ones is similar. In the case of SITVA, it was evidenced that the mixed lane contributes to a high personal exposure to PM2.5 than the exclusive one.

Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs with tens of thousands of stations in Beijing, China. Three types of large-scale PTNs are generated, and the overall network structure of PTNs are examined and found to follow heavy-tailed distributions (mostly power law). Further, three traditional centrality measures (i.e. degree, betweenness and closeness) are adopted and modified to dynamically explore the relationship between PTNs and passenger flow. Our findings show that, the modified centrality measures outperform the traditional centrality measures in estimating passenger flow. [ABSTRACT FROM AUTHOR]

As the world becomes more urbanized, there is a strong need for urban public transport to provide sustainable alternative solutions against private-vehicle usage. However, the opportunities for seamless journeys through public transport are still limited and the need for properly designed and operated transport interchanges is vital. The present paper investigates the perceptions and the users' level of satisfaction when using the New Railway Station of Thessaloniki in Greece and the Riga International Coach Terminal in Latvia, in terms of services provision and station's operation. In total, 36 indicators were tested, grouped in eight quality factors, namely travel information, wayfinding information, time and movement, access, comfort and convenience, station attractiveness, safety and security and emergency situation handling. Attitudinal surveys were implemented to determine key performance factors that affect travelers' satisfaction when using the two terminals. Data were collected through on-line questionnaires and were elaborated through descriptive and inferential statistics, including Mann-Whitney two-sample U-testing to assess differences between the samples in variables measured on a 5-point Likert scale, Spearman bivariate correlations to measure the strength of association between the quality indicators and multiple regression analyses to examine the effect of selected attributes on the general satisfaction level of travelers. Results showed that both interchanges perform better in physical quality attributes, like access, travel and wayfinding information provision, but they do not satisfy users' aesthetics expectations in the internal and external area of the interchanges and the surrounding area and they do not cover adequately their feeling of security and safety in the transfer or waiting areas. These results highlighted the users' preferences and concerns which contribute into a satisfactory overall design of the interchanges. In a nutshell, transport interchange design should satisfy both providing a hub for seamless mobility, but also integrating the station as a part of the public realm. [ABSTRACT FROM AUTHOR]

Relationships between the activities of bus carriers and rail passenger traffic (and the railway offer) are examined in this article. The study was carried out in peripheral areas located at the Polish and Czech borderlands in Lower Silesia province. High quality rail transport generally increases the demand for transport services. Therefore, the proper development of transport offer plays a key role in the functioning of public transport systems, the backbone of which is rail transport. The study also shows that under conditions of transport market deregulation, bus carriers have developed a competitive network which is not complementary to rail transport. As a consequence, the deregulation of the transport market has increased the risk of transport exclusion.

This paper aims at presenting the multiple depot vehicle scheduling problem with heterogeneous fleet and time windows (MDHFVSP-TW). We used a time-space network (TSN) to perform the modeling of MDHFVSP-TW, along with two methodologies to reduce its size and, therefore, its complexity. Along with size reduction methods, a mixed integer programming (MIP) heuristic with variable fixation was presented. Its operation is based on the use of the solution for this problem with relaxed variables as a basis for the removal of arcs from the problem, reducing its size and enabling its resolution in reasonable computational time. Extensive tests were performed for a collection of randomly generated instances. Subsequently, a case study arising from a real instance from a Brazilian city is presented. The computational results showed that the proposed heuristic and size reduction methods obtained good performance, providing high-quality solutions in an adequate computational time. [ABSTRACT FROM AUTHOR]

We present a pioneering investigation into the relation between passenger flow distribution and network properties in public transport systems. The methodology is designed in a reverse engineering fashion by utilizing passively measured passenger flow dynamics over the entire network. We quantify the properties of public transport networks using a range of centrality indicators in the topological representations of public transport networks with both infrastructure and service layers considered. All the employed indicators, which originate from complex network science, are interpreted in the context of public transport systems. Regression models are further developed to capture the correlative relation between passenger flow distribution and several centrality indicators that are selected based on the correlation analysis. The primary finding from the case study on the tram networks of The Hague and Amsterdam is that the selected network properties can indeed be used to approximate passenger flow distribution in public transport systems to a reasonable extent. Notwithstanding, no causality is implied, as the correlation may also reflect how well the supply allocation caters for the underlying demand distribution. The significance and relevance of this study stems from two aspects: (1) the unraveled relation provides a parsimonious alternative to existing passenger assignment models that require many assumptions on the basis of limited data; (2) the resulting model offers efficient quick-scan decision support capabilities that can help transport planners in tactical planning decisions. [ABSTRACT FROM AUTHOR]

Recent studies have shown that public transport users can be exposed to high levels of pollution emitted from their own vehicles and nearby sources. The purpose of this research is to determine the personal exposure of passengers to PM2.5 inside the vehicles of the massive public transport of two of the main and more populated cities of Colombia, Bogotá and Medellín. TM (TransMilenio powered by diesel) and SITVA (electric and gas natural vehicles) were the systems studied. Were evaluated the integration of new vehicles with technologies Euro V and Euro VI in the TM system, the impact of the weekend effect on personal exposure into public transport (TM and SITVA), and the possible differences between personal exposure regarding the ways of the systems (mixed lane or exclusive lane for TM and SITVA). To measure PM2.5 levels, a DustTrak monitor previously calibrated was used. This measurement campaigns lasted for more than 80 hours and a mean of 17000 data of PM2.5 concentrations were obtained for each route. The personal dose was calculated based on the recorded data. The mean PM2.5 concentrations and personal dose found in the research for TM are 167 µg/m³ and 2.3 µg/min, respectively, while, for SITVA they are 41 µg/m³ and 0.53 µg/min, respectively. Therefore, SITVA users have a 5 times lower personal exposure to PM2.5 than TM users. It was also found that due to the poor proportion of new TM vehicles during the monitoring period, the personal exposure in the old vehicles and in the new ones is similar. In the case of SITVA, it was evidenced that the mixed lane contributes to a high personal exposure to PM2.5 than the exclusive one. [ABSTRACT FROM AUTHOR]

Automatic control for recognition of passengers in public transport systems has been a crucial point in mobility systems towards enhancements in passengers' flow and overall system efficiency. It allows the recognition of passengers' origins and destinations, so that the specific demands for specific periods of the day can be assessed for an effective system planning. However, this automatic control has to be efficient and smooth so that it does not incur in additional overhead to the entire system. This work presents a study on a passenger recognition system for public transport through the use of RFID technology using EPC Gen2 standard. Preliminary tests were performed with two different forms of voluntary order to evaluate different types of tags. These tests first evaluated the height and angle of the antennas using 1, 2, 3 and 4 antennas in the tag recognition. From the results of these first tests, a set up was defined and then applied to a second evaluation now with 10 volunteers, which evaluated repeatability and effectiveness of the system for recognition. Moreover, additional laboratory-based tests were performed to access the effectiveness of the proposed recognition system. The acquired results provide a basis for evaluate the suitability and applicability of the proposed system. [ABSTRACT FROM AUTHOR]

The theme of micromobility was introduced for the first time in Thredbo 17 as the growth of shared and privately-owned e-scooters, bicycles and e-bicycles continue to affect the nature and structure of urban transport systems worldwide. And whilst in some cases they challenge the priority afforded to the private car, in others they complement already existing and well-established greener transport modes such as cycling and walking. The discussion in this workshop focused on a number of questions looking at the benefits of micromobility and discussing the main incentives for their use as an urban mode of transport, questioning the role of government and describing the potential threats, if any, to public transport systems, in what we expect for the future of micromobility. Five papers showcased evidence on the use of micromobility, from e-scooters in Norway and Australia, to public bicycles in South Korea and The Netherlands. Each of the questions discussed in the workshop is reported in this paper. The need for multimodal integration is evident and remains essential to ensure complementarity across transport in cities. However, other concerns such as the need for regulation, education/enforcement structures, stronger business models and more effective tendering procedures have been identified and discussed. A long list of future research topics in the area of micromobility is provided and some themes for Thredbo 18 are recommended. [ABSTRACT FROM AUTHOR]

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

Bus transport systems cannot retain scheduled headways without feedback control due to their unstable nature, leading to irregularities such as bus bunching, and ultimately to increased service times and decreased bus service quality. Traditional anti-bunching methods considering only regularization of spacings might unnecessarily slow down buses en route. In this work a detailed but computationally lightweight dynamical model of a single line bus transport system involving both continuous and binary states is developed. Furthermore a hybrid model predictive control (MPC) scheme is proposed, with a dual objective of regularizing spacings and improving speed of bus service operations. Performance of the predictive controller is compared with I- and PI-controllers via extensive simulations using the proposed model. Results indicate the potential of the hybrid MPC in avoiding bus bunching and decreasing passenger delays inside and outside the buses. [ABSTRACT FROM AUTHOR]

Transport systems and buildings are among the bigger energy users inside cities. Abundant research has been developed about these systems (facilities and transport). However, synergies among them are commonly overlooked, not taking advantage of the possible benefits of their joint coordination and management. This paper presents a linear programming model to find the optimal operation and planning of distributed energy resources (DER) in a residential district, while considering electric private and public transport systems, in particular electric vehicles and metro. Hence, the main contribution of this paper is the analysis of synergies of such an interconnected scheme. It has been assumed that part of the metro regenerative braking energy can be stored into electric vehicles’ (EVs’) batteries, so that it can be used later for other trains or for the EV itself. Several case studies have been proposed using data from a residential district and a metro line in Madrid. The obtained results show important cost savings in the overall system, especially a significant power cost reduction for the metro system. [ABSTRACT FROM AUTHOR]

Cities worldwide are implementing modern transit systems to improve mobility in the increasingly congested metropolitan areas. Despite much research on the effects of such systems, a comparison of effects across transit modes and countries has not been studied comprehensively. This paper fills this gap in the literature by reviewing and comparing the effects obtained by 86 transit systems around the world, including Bus Rapid Transit (BRT), Light Rail Transit (LRT), metro and heavy rail transit systems. The analysis is twofold by analysing (i) the direct operational effects related to travel time, ridership and modal shifts, and (ii) the indirect strategic effects in terms of effects on property values and urban development. The review confirms the existing literature suggesting that BRT can attract many passengers if travel time reductions are significantly high. This leads to attractive areas surrounding the transit line with increasing property values. Such effects are traditionally associated with attractive rail-based public transport systems. However, a statistical comparison of 41 systems did not show significant deviations between effects on property values resulting from BRT, LRT and metro systems, respectively. Hence, this paper indicates that large strategic effects can be obtained by implementing BRT systems at a much lower cost. [ABSTRACT FROM PUBLISHER]

The smart city seeks a highly interconnected, monitored and globally optimized environment to profit from the synergies among systems such as energy, transports or waste management. From an energy perspective, transport systems and facilities are among the bigger energy consumers inside cities. However, despite the research available on such systems, few works focus on their interactions and potential synergies to increase their efficiencies. This paper address this problem by assessing the benefits of the interconnection and joint management of different energy systems in a smart city context. This is done using a linear programming problem, modelling a district with residential loads, distributed energy resources (DER) and electric vehicles (EV), which are also connected to an electrical metro substation. This connection allows to store the metro regenerative braking energy into EVs' batteries to be used later for other trains or for the EVs themselves. The objective of the linear programming model is to find the optimal planning and operation of all the considered systems, achieving minimum energy costs. Therefore, the main contributions of this paper are the assessment of synergies of the interconnection of these systems and the detailed analysis of the impact of different EV penetration levels. Results show important economic benefits for the overall system (up to 30%) when the investments and its operation are globally optimized, especially reducing the metro energy costs. Also, analysing the energy transfers between metro-EV, it is evident that the metro takes advantages of the cheaper energy coming from the district (through the EVs), showing the existence of “opportunistic” synergies. Lastly, EV saturation points (where extra EVs represent more load but do not provide additional useful storage to the system) can be relatively small (200–300 EVs) when the energy transfer to the metro electrical substation is restricted, but it is also reduced by the presence of DER systems. [ABSTRACT FROM AUTHOR]

Quality is an essential aspect of public transport. In the case of regular public passenger transport by road, punctuality and regularity are criteria used to assess quality of service. Calculating metrics related to these criteria continuously over time and comprehensively across the entire transport network requires the handling of large amounts of data. This article describes a system for continuously and comprehensively monitoring punctuality and regularity. The system uses location data acquired continuously in the vehicles and automatically transferred for analysis. These data are processed intelligently by elements that are commonly used by transport operators: GPS-based tracking system, onboard computer and wireless networks for mobile data communications. The system was tested on a transport company, for which we measured the punctuality of one of the routes that it operates; the results are presented in this article. [ABSTRACT FROM AUTHOR]

Trams are playing increasingly important role in today's public transport systems, as they are punctual and friendly to the environment, in addition to high capacity compared to buses. But the normal function of this above-ground light rail system can be interrupted by unexpected events. This research adopts the disruption recovery approach used in Munich and Berlin, Germany – the best service acclaimed by the public, to examine the collaboration mechanism with a taxi company to provide fast recovery service during tram disruptions. Because of the spontaneous nature of disruptions, a proactive contract-based collaboration strategy is formulated. The two involving parties’ decision functions are developed and analyzed both individually and interactively as a game to generate the optimal solutions. Numerical sensitivity analysis of critical parameters are conducted to shed more lights on decision-making and implementation. [ABSTRACT FROM AUTHOR]

This paper investigates the potential temporal and spatial variations of suicides in subway stations in Stockholm, Sweden. The study also assesses whether the variation in suicide rates is related to the station environments by controlling for each station’s location and a number of contextual factors using regression models and geographical information systems (GIS). Data on accidents are used as references for the analysis of suicides. Findings show that suicides tend to occur during the day and in the spring. They are concentrated in the main transportation hubs but, interestingly, during off-peak hours. However, the highest rates of suicides per passenger are found in Stockholm’s subway stations located in the Southern outskirts. More than half of the variation in suicide rates is associated with stations that have walls between the two sides of the platform but still allow some visibility from passers-by. The surrounding environment and socioeconomic context show little effect on suicide rates, but stations embedded in areas with high drug-related crime rates tend to show higher suicide rates. [ABSTRACT FROM AUTHOR]

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• Traditional time series forecasting ignores the causal structure in passenger demand. • We introduce a novel RPP concept to encode such long-range dependency. • We demonstrate the effectiveness of this approach using real-world data. • The single covariate greatly enhances forecasting accuracy for most stations. Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler. For example, a traveler's work trip in the morning can help predict his/her home trip in the evening, while this causal structure cannot be explicitly encoded in standard time series models. In this paper, we propose a new forecasting framework for boarding flow by incorporating the generative mechanism into standard time series models and leveraging the strong regularity rooted in travel behavior. In doing so, we introduce returning flow from previous alighting trips as a new covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow—a single covariate—can substantially and consistently improve various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. And the proposed method is more effective for business-type stations with most passengers come and return within the same day. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and long-range dependencies are generated by the user behavior. [ABSTRACT FROM AUTHOR]