Oded Cats

Impacts of holding control strategies on transit performance : A bus simulation model analysis

Transit operators are interested in strategies to improve service reliability as it is an important measure of performance and level of service. One of the common practices aimed at reducing service unreliability is holding control strategies. The design of these strategies involves the selection of a set of time point stops and the holding criteria for regulating the departure time. The interactions between passenger activity, transit operations, and traffic dynamics must be dynamically modeled to analyze the impacts of holding strategies on transit performance. An evaluation of different holding criteria and the number and location of time point stops was conducted with BusMezzo, a dynamic transit simulation model. The holding strategies were implemented in the model and applied to a high-frequency trunk bus line in Stockholm, Sweden. The analysis of the results considers the implications of holding strategies from both passenger and operator perspectives. The analysis suggests substantial gains are possible by implementing a holding strategy on the basis of the mean headway from the preceding and the succeeding buses. This strategy is the most efficient for passenger time savings as well as fleet costs and crew management.

Effect of Real-Time Transit Information on Dynamic Path Choice of Passengers

Real-time information (RTI) is increasingly being implemented in transit networks worldwide. The evaluation of the effect of RTI requires dynamic modeling of transit operations and of passenger path choices. The authors present a dynamic transit analysis and evaluation tool that represents timetables, operation strategies, RTI, adaptive passenger choices, and traffic dynamics at the network level. Transit path choices are modeled as a sequence of boarding, walking, and alighting decisions that passengers undertake when carrying out their journey. The model was applied to the Metro network area of Stockholm, Sweden, under various operating conditions and information provision scenarios, as a proof of concept. An analysis of results indicated substantial path choice shifts and potential time savings associated with more comprehensive RTI provision and transfer coordination improvements.

Mesoscopic modeling of bus public transportation

Analysis of public transport system performance and level of service in urban areas is essential. Dynamic modeling of traffic conditions, passenger demand, and transit operations is important to represent adequately the complexity of and the interactions between these components in modern public transportation systems. This paper presents a transit simulation model designed to support evaluation of operations planning and control, especially in the context of advanced public transportation systems. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems, taking into account the main sources of service uncertainty and stochasticity. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world, high-demand bus line in metropolitan Tel Aviv, Israel, under various scenarios. The application shows that important phenomena such as bus bunching are reproduced realistically. A comparison of simulated running times and headway distributions with field data shows the model is capable of replicating observed data.

The value of new public transport links for network robustness and redundancy

A common argument for introducing new links or services to transport networks is that they will contribute to greater capability to withstand system breakdowns. This paper presents a methodology for assessing the value of new links for public transport network robustness, considering disruptions of other lines and links as well as the new links themselves. The value is evaluated in terms of passenger welfare under disruptions and can be compared to traditional welfare benefits and investment costs. Distinction is made between the value of robustness, defined as the change in welfare during disruption compared to the baseline network, and the value of redundancy, defined as the change in welfare losses due to disruption. The paper introduces the total values of robustness and redundancy by considering a full space of scenarios and their respective frequencies. Using a model that considers passengers’ dynamic travel choices, stochastic traffic conditions, timetables and capacity constraints, results are more nuanced than analyses based only on network topology and other static attributes. A new cross-radial light rail transit line in Stockholm, Sweden, is evaluated. The new link increases welfare levels under all scenarios and has a positive value of robustness. However, disruption costs increase under some scenarios and the value of redundancy is negative. In general, the value of redundancy depends on the new links role as complement or substitute and passengers’ ability to utilise spare capacity during short-term unexpected disruptions.

Multi-agent transit operations and assignment model

Transit systems exercise complex dynamics and evolve through the interaction of various agents. The analysis of transit performance requires emulating the dynamic loading of travellers and their interaction with the underlying transit system. Multi-agent simulations aim to mimic the emergence of global spontaneous order from numerous inter-dependent local decisions. This paper presents a framework for a multi-agent transit operations and assignment model which captures supply uncertainties and adaptive user decisions. An iterative day-to-day learning process consisting of a within-day dynamic network loading loop simulates the interaction between transit supply and demand. The model requires the development and integration of several modules including traffic simulation, transit operations and control, dynamic path choice model and real-time information generator. BusMezzo, a transit simulation model, is used as the platform for implementation.

Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty

Public transport systems are subject to uncertainties related to traffic dynamic, operations, and passenger demand. Passenger waiting time is thus a random variable subject to day-to-day variations and the interaction between vehicle and passenger stochastic arrival processes. While the provision of real-time information could potentially reduce travel uncertainty, its impacts depend on the underlying service reliability, the performance of the prognosis scheme, and its perceived credibility. This paper presents a modeling framework for analyzing passengers’ learning process and adaptation with respect to waiting-time uncertainty and travel information. The model consists of a within-day network loading procedure and a day-to-day learning process, which are implemented in an agent-based simulation model. Each loop of within-day dynamics assigns travelers to paths by simulating the progress of individual travelers and vehicles as well as the generation and dissemination of travel information. The day-to-day learning model updates the accumulated memory of each traveler and updates consequently the credibility attributed to each information source based on the experienced waiting time. A case study in Stockholm demonstrates model capabilities and emphasizes the importance of behavioral adaptation when evaluating alternative measures which aim to improve service reliability.

Public transport pricing policy: Empirical evidence from a fare-free scheme in Tallinn, Estonia

Cities worldwide are looking for new policies to attract travelers to shift from cars to public transport. Policies focused on reducing public transport fares are aimed at improving social inclusion and leading to a modal shift. The City of Tallinn, the capital of Estonia, has recently introduced a fare-free public transport (FFPT) service in an effort to improve accessibility and mobility for its residents. The case of Tallinn is a full-scale, real-world experiment that provides a unique opportunity for investigating the impacts of FFPT policy. A macrolevel empirical evaluation of FFPT impacts on service performance, passenger demand, and accessibility for various groups of travelers is presented. In contrast to previous studies, the influence of FFPT on passenger demand was estimated while changes in supply were controlled. The results indicate that the FFPT measure accounts for an increase of 1.2% in passenger demand, with the remaining increase attributed to an extended network of public transport priority lanes and increased service frequency. The relatively small effect could be attributed to the previous price level and public transport share as well as analysis of the short-term impact. The evidence-based policy evaluation in this paper is instrumental in supporting policy making and facilitating the design of public transport pricing strategies.

Optimizing the number and location of time point stops

Public transport service is subject to multiple sources of uncertainty that impact its reliability. Holding control strategies are a common method to prevent the deterioration of service reliability along the route. This paper expands on previous studies by considering the general case of determining both the optimal number and optimal location of the time point stops (TPS) where holding takes place, and assessing their impacts on transit performance using simulation. Holding times are determined based on a real-time headway-based holding strategy designed to improve service regularity by seeking uniform headways along the route. The evaluation of the performance of alternative TPS layouts is simulation-based, using BusMezzo, a transit operations simulation model which models the dynamic performance of bus transit systems. The proposed framework also considers the multiple objectives incorporating passenger and operator points of view. The simulation-based optimization framework was applied in a case study with one of the premium bus lines in Stockholm, Sweden, using two solution methods—greedy and genetic algorithms. A multi-objective evaluation was conducted considering both passenger and operator perspectives. The results demonstrate that transit performance varies considerably with alternative TPS layouts. The best solution obtained by the proposed methodology reduces total weighted passenger journey times and cycle times compared to both the current layout and the case of no holding control. The proposed method could assist transit agencies and operators in evaluating and recommending alternative time point layouts.

Beyond a complete failure: the impact of partial capacity degradation on public transport network vulnerability

Disruptions in public transport networks (PTNs) often lead to partial capacity reductions rather than complete closures. This study aims to move beyond the vulnerability analysis of complete failures by investigating the impacts of a range of capacity reductions on PTN performance. The relation between network performance and the degradation of line or link capacities is investigated by establishing a vulnerability curve and related metrics. The analysis framework is applied to a full-scan analysis of planned temporary line-level capacity reductions and an analysis of unplanned link-level capacity reductions on the most central segments in the multi-modal rapid PTN of Stockholm, Sweden. The impacts of capacity reductions are assessed using a non-equilibrium dynamic public transport operations and assignment model. The nonlinear properties of on-board crowding, denied boarding, network effects and route choice result in non-trivial, generally convex, relations which carry implications on disruption planning and real-time management.

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

New big data (sources) in the public transport industry enable to deal with major challenges such as elevating efficiency, increasing passenger ridership and satisfaction and facilitate the information flow between service providers and service users. This paper presents two actual cases from the Netherlands and Sweden in which automated data sources were utilized to support the planning and operational processes. The cases illustrate the benefits of using smartcard and vehicle positioning data. Due to the data (processing), valuable insights were gained helping to make the right choices and improve the public transport system.