Keys to effective transit strategies for commuting

Commuting poses relevant challenges to cities\u2019 transport systems. Various studies have identified transit as a tool to enhance sustainability, efficiency and quality of the commute. The scope of this paper is to present strategies that increase public transport attractiveness and positively impact its modal share, looking at some case studies and underlining key success factors and possible elements of replica to be ultimately planned in some of the contexts of the Interreg project SMART-COMMUTING. The strategies analyzed in this paper concern prices and fares, service expansion, service improvements, usage of vehicle locators and other technology, changes to the built environment. Relevant gains in transit modal share are more easily achievable when considering integrations between various strategies, thus adapting and tailoring the planning process to the specific context

Analysing improvements to on-street public transport systems: a mesoscopic model approach

Light rail transit and bus rapid transit have shown to be efficient and cost-effective in improving public transport systems in cities around the world. As these systems comprise various elements, which can be tailored to any given setting, e.g. pre-board fare-collection, holding strategies and other advanced public transport systems (APTS), the attractiveness of such systems depends heavily on their implementation. In the early planning stage it is advantageous to deploy simple and transparent models to evaluate possible ways of implementation. For this purpose, the present study develops a mesoscopic model which makes it possible to evaluate public transport operations in details, including dwell times, intelligent traffic signal timings and holding strategies while modelling impacts from other traffic using statistical distributional data thereby ensuring simplicity in use and fast computational times. This makes it appropriate for analysing the impacts of improvements to public transport operations, individually or in combination, in early planning stages. The paper presents a joint measure of reliability for such evaluations based on passengers’ perceived travel time by considering headway time regularity and running time variability, i.e. taking into account waiting time and in-vehicle time. The approach was applied on a case study by assessing the effects of implementing segregated infrastructure and APTS elements, individually and in combination. The results showed that the reliability of on-street public transport operations mainly depends on APTS elements, and especially holding strategies, whereas pure infrastructure improvements induced travel time reductions. The results further suggested that synergy effects can be obtained by planning on-street public transport coherently in terms of reduced travel times and increased reliability

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

A decision support system to improve service quality in multimodal rapid rail systems: A bayesian perspective

Copyright @ 2011 International Conference on Computers and Industrial EngineeringIn this study, the accessibility of the rail transit stations in a multimodal network formed by a trunk line and its feeder lines are defined. Connectivity between lines and the accessibility of the nodes identify the overall spatial structure of the network. The factors influencing the access choices of rail transit stations and satisfaction of transit travelers in rapid rail transit systems are investigated in order to gain insights into the factors and their interrelationships. The quantitative indications of the relationships are produced and the complexity of evaluating the performance of transit services is exhibited. As the interrelationships are mainly stochastic, the problem on hand is treated as a Bayesian Belief Network (BBN). A BBN approach that presents a learning mechanism is employed and is used as an alternative decision making tool to analyze the rapid rail transit services and identify policies to improve the traveler’s level of service

Investigation to enhance sustainable improvements in high speed rail transport

Transport systems are essential for the life of modern societies and economies. A sustainable transport system can shape a sustainable development pattern and socio-economic attributes of urban centres and regions. The use of private cars and trucks is increasing in most countries, creating more congestion, accidents, pollution and energy consumption. Many governments desire to achieve growth in public transport to overcome these adverse trends. A massive shift toward an environmentally sound type of transport is crucial and railways are deemed to be one of the most sustainable modes. All over the world the railway industry is involved in a renewal to reform and up-date rail, prompted largely by environmental concerns. The trend is to develop speed-competitive systems to expand transportation capacity. The focus of the current research, which is at its commencing stages, is to investigate the opportunities to apply an alternative approach to railway operations to overcome the difficulty of high speed transport in servicing larger amounts of demand, while achieving minimum point to point travel time, in a viable and integrated environment for both passenger and freight services. The expected outcome of the research project is to present a framework that may be used to identify and evaluate the most cost-effective transport solution to service not only major cities, but also regional centres along an interregional rail corridor, thus providing greater benefits on local economies and to build a spine for future development

Reliable routing in schedule-based transit networks

textA framework is proposed for determining the least expected cost path in a schedule-based time-expanded public transit network where travel times, and thus bus arrival and departure times at stops, are stochastic. Transfer reliability is incorporated in a label-correcting algorithm with a penalty function for the expected waiting time when transferring that reflects the likelihood of making a successful transfer. The algorithm is implemented in transit assignment on an Austin, Texas test network, using actual bus arrival and departure time distributions from vehicle location data. Assignment results are compared with those of a deterministic shortest path based on the schedule and from a calibrated transit assignment model. Simulations of the network and passenger paths are also conducted to evaluate the overall path reliability. The reliable shortest path algorithm is found to penalize transferring and provide paths with improved transfer and overall reliability. The proposed model is realistic, incorporating reliability measures from vehicle location data, and practical, given the efficient shortest path approach and application to transit assignment.Civil, Architectural, and Environmental Engineerin

Passengers’ choices in multimodal public transport systems : A study of revealed behaviour and measurement methods

The concept of individual choice is a fundamental aspect when explaining and anticipating behavioural interactions with, and responses to, static and dynamic travel conditions in public transport (PT) systems. However, the empirical rounding of existing models used for forecasting travel demand, which itself is a result of a multitude of individual choices, is often insufficient in terms of detail and accuracy. This thesis explores three aspects, or themes, of PT trips – waiting times, general door-to-door path preferences, with a special emphasis on access and egress trip legs, and service reliability – in order to increase knowledge about how PT passengers interact with PT systems. Using detailed spatiotemporal empirical data from a dedicated survey app and PT fare card transactions, possible cross-sectional relationships between travel conditions and waiting times are analysed, where degrees of mental effort are gauged by an information acquisition proxy. Preferences for complete door-todoorpaths are examined by estimation of full path choice models. Finally, longitudinal effects of changing service reliability are analysed using a biennial panel data approach. The constituent studies conclude that there are otherexplanatory factors than headway that explain waiting times on first boarding stops of PT trips and that possession of knowledge of exact departure times reduces mean waiting times. However, this information factor is not evidentin full path choice, where time and effort-related preferences dominate with a consistent individual preference factor. Finally, a statistically significant on-average adaption to changing service reliability is individual-specific andnon-symmetrical depending on the direction of reliability change, where a relatively large portion of the affected individuals do not appear to respond to small-scale perturbations of reliability while others do, all other thingsbeing equal

Traveler Responses to Real-Time Transit Passenger Information Systems

In recent years, a considerable amount of money has been spent on Real-time Transit Passenger Information Systems (RTPISs), which provide timely and accurate transit information to current and potential riders to enable them to make better pre-trip and en-route decisions. Understanding traveler responses to real-time transit information is critical for designing such services and evaluating their effectiveness. To answer this question, an effort is made in this dissertation to systematically conceptualize a variety of behavioral and psychological responses travelers may undertake to real-time transit information and empirically examine the causal effects of real-time information on traveler behavior and psychology. This research takes ShuttleTrac, a newly implemented real-time bus arrival information system for UMD's Shuttle-UM service, as a case for empirical study. In Part 1 analysis, using panel datasets derived from three-waved online campus transportation surveys, fixed-effects OLS models and random-effects ordered probit models are estimated to sort out causal relations between ShuttleTrac information use and general/cumulative behavioral and psychological outcomes. In addition, a two-stage instrumental variable model was estimated to examine the potential change in habitual mode choices due to real-time transit information use. The results show that with a few months of adjustment, travelers may increase their trip-making frequency as a result of real-time transit information use, and positive psychological outcomes are more prominent in both short and longer terms. In Part 2 analyses, using the cross-sectional dataset derived from the onboard survey, OLS models and ordered logit models were estimated to examine the trip-specific psychological effects of real-time transit information. The results show that these trip-specific psychological effects of real-time transit information do exist in expected directions and they vary among user groups and in different scenarios. A finding consistent across two parts of analyses is that accuracy of information plays a greater role in determining traveler behavior and psychology than the mere presence. This research contributes to the general discussion on traveler behavior under advanced information by 1) developing an integrative conceptual framework; and 2) providing useful insights into the issue with much empirical evidences obtained with revealed-preference data and sophisticated modeling techniques

Demand Modeling for Taxi and Ride-hailing Transport Services (RTS)

The rapid growth of Ride-hailing Transport Services (RTS) demand is found to have caused a fierce market share battle with conventional taxis in previous decades. In selecting a taxi or RTS, understanding the factors affecting passenger’s decisions is substantial for better development and more reliable transit service. The aims of this study to evaluate the demand for taxis and RTS in the Jakarta Greater Area, Indonesia, using the demand-supply and dynamic models. It has been conducted by using 519 respondents, with the model inputs consisting of waiting and travel time, trip costs, and the destination of the conventional passengers. Moreover, the choice between taxi and RTS was analyzed based on the stated preferences of respondents. The results showed that the waiting and travel time, as well as costs per trip of RTS, were 1.49 and 2.67 minutes lower and IDR10,902 cheaper than a taxi, respectively. The factors influencing the demand for these transport modes were also the number of trips per-day, mode share, the average vehicle occupancy, operating hours/day, passengers and driver waiting time, as well as travel period. In the dynamic model, the addition of variable service area, peak hour, and average vehicles speed was subsequently observed. Based on the results, the requests for these transport modes in the Greater Area of Jakarta were 64,494 and 55,811 vehicle units for the demand-supply and dynamic models, respectively. This proved that the dynamic model was better than the demand-supply, due to the added parameters representing the area’s traffic characteristics. Additionally, subsequent future research are expected to focus on modeling of taxi and RTS demands through the global positioning system data, as well as analysis using machine learning and deep learning. Doi: 10.28991/CEJ-2023-09-05-03 Full Text: PD