Fabien Leurent

Estimating Distributions of Walking Speed, Walking Distance, and Waiting Time with Automated Fare Collection Data for Rail Transit

Journey time is one of the key factors of public transport quality of service that is of concern to passengers. The variability in passenger journey time stems from the variability of in-vehicle travel time, walking time, and waiting time. A better understanding of passenger walking and waiting behavior along an urban rail transit line, especially in mass transit hubs and stations, is thus of much relevance. However, the estimation of passengers’ walking speed, walking distance, and waiting time is still a complicated and difficult task: individual walking speed and waiting time keep changing throughout the interindividual journey. A novel stochastic model that uses automated fare collection data and automatic vehicle location data is proposed to estimate the distributions of walking speed, walking distance, and waiting time indirectly. The stochastic model relates tap-out time to tap-in time on an individual basis and with respect to train circulation on the basis of statistical distributions for the individual’s cruise walking speed, in-station walking distance, and waiting time. Analytical formulas are provided, first conditional to an individual walking speed and waiting time and then without conditions. The model is applied to the maximum likelihood estimate (MLE) of the parameters with constrained numerical optimization. A case study of the urban rail transit line Réseau Express Régional [Regional Express Network (RER)] A in the Paris region yielded reasonable parameter values and factor mean values. This study paves the way for estimating passenger elementary travel time along a journey.

Influencing Longitudinal Passenger Distribution on Railway Platforms to Shorten and Regularize Train Dwell Times

The longitudinal distribution of passengers waiting on a train platform influences the boarding and alighting time. A smoother, more uniform distribution could benefit both traffic operations and passenger experiences. This paper investigates pedestrian traffic performance and the train operations that it influences in an integrated way by proposing (a) two in situ solutions to inform passengers and influence their waiting position on the platform, (b) a specific survey of passenger behavior under these conditions, (c) a modeling scheme based on a pedestrian microsimulation, and (d) an example of application to a suburban rail station in eastern Paris, the Noisy–Champs Station on the Réseau Express Régional (RER) A Line. The example reveals a traffic phenomenon of corridor–car interplay that implicates the specific behavior of late passengers and the contribution of this phenomenon to train dwell time.

Applications and Future Developments: Modeling Software and Advanced Applications

Situation. Part III and Chap. 8 are targeted to the theoretical modeling of transit systems. The challenge is to match the model’s scope and contents to the real-world features that are relevant to the simulation purposes. As the theoretical development of models is oriented toward the development of scientific knowledge, it takes place mostly in the research arena—research teams, academic network, and scientific reviews.

Spatial refinement to better evaluate mobility and its environmental impacts inside a neighborhood

A large share of a neighborhood project’s environmental impacts is due to mobility. It either takes place inside the neighborhood, such as transit traffic or internal mobility, or is induced by it and exchanged with the rest of the urban area. A way to improve mobility impacts evaluation in the assessment of neighborhood alternative designs, is to refine traffic simulation models making them more sensitive to spatial design while keeping their sensitivity to local traffic conditions and associated energy consumption and pollutants’ emissions. This paper introduces a methodology relying on the classic four-step scheme for mobility demand modelling together with specific spatial refinement. The neighborhood is divided into fine sub-areas, with specific consequences for each step: first, trips are generated on the basis of sub-area land-use and activity data; second, the trips are distributed between all Traffic Analysis Zones (TAZs), enabling to identify internal short-range trips; third, the mode choice model takes into account the particular access conditions between sub-areas and transit stations or roadway nodes; fourth, traffic assignment involves finer TAZs and finer path description. Furthermore, a 5th step is added to deal with environmental evaluation, especially the allocation of mobility impacts to the project’s sub-areas. These steps are presented and illustrated on the ‘Cite Descartes’ district case study, in Eastern Paris. Dividing its 1 km² area into about 100 sub-areas enabled us to depict the projects’ program and spatial layout very finely, especially so in relation to the transit stops and stations location. Some limitations and needs for further research are also outlined.

Who is the Expert? Integrated Urban Modeling and the Reconfiguration of Expertise

ABSTRACT Much has been spoken and written about the rationalization and optimization of services and amenities in urban territories. In this context, there is increasing use of numerical modeling techniques addressing the design, selection and/or calibration of policy instruments. The question of the relations between appraisal tools and policymaking has been widely studied. However, few studies have specifically focused on the role of modeling in policymaking processes. Drawing on two case studies, this paper suggests a change in the nature of multi-expertise: neither conflicting nor cross-sectoral, we observed in both cases an interwoven configuration with a network of experts making use of integrated models. We call this configuration distributed expertise, arguing that it is a novel configuration and that its emergence is closely linked to the development of integrated modeling techniques. Other authors have discussed the idea that the growing need for new appraisal tools is linked with the proliferation of wicked policy problems. From our case studies we would conclude that the emergence of integrated modeling is not a response to complex problems but to complex systems of actors who need to reach a consensus on actions.

Managing planned disruptions of mass transit systems

Unplanned disruptions of rail transit networks have been studied extensively. Planned disruptions for works essentially are different mainly because of their longer duration, which allows passengers to build alternative route choice strategies. The literature on this topic remains scarce. In this study, a novel methodology was proposed to enable operators to evaluate different disruption management schemes and to obtain explicit estimations of travel times, passenger comfort flows, and levels of service. Statistical tools were used to evaluate the different strategies. The methodology is illustrated here through a large-scale application to a real line disruption in Paris. The disruption took place in July 2015 as the result of network maintenance work and affected Line A of Réseau Express Régional, a major suburban railway line that provides more than 1 million trips on a typical working day. Study results indicated that the disruption would have significantly increased the generalized cost (GC) of passengers if no action had been taken. The operator’s disruption management scheme included bus bridging and increases in service frequency on alternative routes. Evaluation showed that this plan restored the average GC across the whole network. Passengers who initially used the disrupted line experienced increased GC when they used the longer, alternative routes. Passengers who initially used those alternative routes experienced lower GC as a result of the increase in service frequency. Finally, capacity problems were observed on the buses that ensured a bridge across the disrupted link.

Applications and Future Developments: Future Developments and Research Topics

This final chapter explores the potential of traffic assignment models for development, beyond traditional or advanced applications. The modeling of public transportation systems is a fertile terrain for research, especially as the digital era is seeing a proliferation of innovations: in the operation of existing systems and above all in the design of original, flexible, demand-responsive mobility services, which rely on different forms of resource pooling. The principle of pooling, fundamental in mass transit for the sharing of infrastructures by vehicles and the sharing of vehicles by passengers, has now found a very wide range of applications, thanks to the presence of information everywhere in the mobility system, which includes the transportation system and its users. The body of the chapter is structured into three sections. First, we consider the new deal in public urban passenger transport that stems from the new order in the field of information: Ongoing or future innovations pertain to the management of line networks, to the provision of more flexible intermediate services, and to the sharing of vehicles, drivers, and parking spaces, together with the potential associated with autonomous (automated self-driving) vehicles. Second, we identify a whole range of research topics on traffic assignment models and their inputs to their potential applications for system regulation, passing by (i) passenger behaviors and their statistical structures, (ii) the physics and control of traffic—both passengers and vehicles, (iii) the spatial features and their flow-oriented layout, and (iv) the organization and operations of specific travel modes. Third and last, we open up a broad perspective onto the relation between mobility systems and simulation models: Models are becoming more and more modular, and they constitute a toolbox that is more and more powerful; a number of tools are implemented to bring augmented reality to the transit systems for all of its stakeholders (users, operators, regulators, general public); arguably, an Urban Mobility Living Lab should be an ideal framework to study system conditions, to design user-oriented innovations, and to test system’s responses to them on the field.

From Transit Systems to Models: Purpose of Modelling

From Part I of the book, it will be obvious that public transport plays an essential role in providing mobility to people, especially in dense urban areas. The social welfare generated by good public transport comes at a price, however. Almost all forms require large investments into infrastructure, vehicles and operation. With limited finance, ideal public transport remains a distant goal, and a lot of effort goes into finding an optimal allocation of budget to investment options. The key question for these decisions is: How big is the total benefit of a proposed investment? To answer it, one needs to predict how the potential users will make use of the hypothetical improved public transport. For responsible decision-making, this prediction should be rational, transparent and accountable. It is no surprise therefore that models are typically used to produce the predictions. These models span the whole range of mobility decisions made by individuals, from long term to short term. Passenger route choice, the focus of Part III, accounts for only a part of the complex decision hierarchy. Before zooming into route choice models, this chapter looks at the planning process as a whole, explains the role of models in decision-making and gives an overview of the whole decision hierarchy. The last two sections introduce the general mathematical framework, in which decision models are formulated and set the stage for the description of specific models.

Applications and Future Developments: Modelling the Diversity and Integration of Transit Modes

Passenger transit modes typical of the urban setting, including bus, tram, metro, and train, have been described in Chap. 2, along with less conventional modes such as BRT and cable. Then, Chaps. 6 and 7 have provided network assignment models that address primarily the passenger side, dealing with route choice situations and behaviour, the individual exposure to traffic conditions, and the contribution of individual users to local flows. In these models, the transit mode is represented as a set of lines, each of which is abstracted into its topology (nodes and links) and some features of traffic operations: run time, dwell time, and some capacity parameters. In such an abstract setting, no distinction has been made between, for instance, bus and railway services, apart from their respective parameter values.

A transit bottleneck model for waiting passengers and its implications for traffic assignment

The paper addresses the issue of passenger waiting at a station platform, from which they plan to board transit services towards egress stations. Each transit service has a specific set of downstream egress stations and is operated at given frequency using homogeneous vehicles of limited available capacity. We propose a static model that yields “passenger stocks” on the platform and individual waiting times by egress station, as well as the assignment of vehicle capacity to the flows by egress station. Two cases are considered; unsaturated versus saturated. The unsaturated case is addressed by standard line combination, where service frequency is added up among the routes that serve a given egress station. The saturated case is addressed by making explicit the average number of passengers waiting on platform for a given egress station. From these passenger stocks, we deduce the individual probability to board a vehicle of limited capacity that serves a given route (hence a given subset of egress stations). Waiting passengers are assumed to be mingling on the origin platform. The subset of routes that serve a given egress station, their vehicle capacities along with the associated boarding probabilities give a line capacity by destination. The bottleneck model confronts this capacity to demand during the assignment period and provides both the stock of waiting passengers and an average waiting time per passenger. The vector of passenger stocks by egress station is shown to satisfy a fixed point problem. The existence of a solution is demonstrated.