Waiting time perceptions at transit stops and stations: Effects of basic amenities,gender, and security

Waiting time in transit travel is often perceived negatively and high-amenity stops and stations are becoming increasingly popular as strategies for mitigating transit riders’ aversion to waiting. However, beyond recent evidence that realtime transit arrival information reduces perceived waiting time, there is limited empirical evidence as to which other specific station and stop amenities can effectively influence user perceptions of waiting time. To address this knowledge gap, the authors conducted a passenger survey and video-recorded waiting passengers at different types of transit stops and stations to investigate differences between survey-reported waiting time and video-recorded actual waiting time. Results from the survey and video observations show that the reported wait time on average is about 1.21 times longer than the observed wait time. Regression analysis was employed to explain the variation in riders’ reported waiting time as a function of their objectively observed waiting time, as well as station and stop amenities, weather, time of the day, personal demographics, and trip characteristics. Based on the regression results, most waits at stops with no amenities are perceived at least 1.3 times as long as they actually are. Basic amenities including benches and shelters significantly reduce perceived waiting times. Women waiting for more than 10 min in perceived insecure surroundings report waits as dramatically longer than they really are, and longer than do men in the same situation. The authors recommend a focus on providing basic amenities at stations and stops as broadly as possible in transit systems, and a particular focus on stops on low-frequency routes and in less safe areas for security measures.     

The dynamics of fare and frequency choice in urban transit

This paper investigates the choice of fare and service frequency by urban mass transit agencies. A more frequent service is costly to provide but is valued by riders due to shorter waiting times at stops, and faster operating speeds on less crowding vehicles. Empirical analyses in the 1980s found that service frequencies were too high in most of the cities studied. For a given budget constraint, social welfare could be improved by reducing service frequencies and using the money saved to lower fares. The cross-sectional nature of these analyses meant that researchers were unable to address the question of when the oversupply occurred. This paper seeks to answer that question by conducting a time-series analysis of the bus operations of the Chicago Transit Authority from 1953 to 2005. The paper finds that it has always been the case that too much service frequency was provided at too high a fare. The imbalance between fares and service frequency became larger in the 1970s when the introduction of operating subsidies coincided with an increase in the unit cost of service provision.     

Passengers' perceptions and effects of bus‐holding strategy using automatic vehicle location technology

This study reports bus passengers' behavior and perceptions related to the use of potential features of an automatic vehicle location (AVL) system in bus transit through conducting an attitudinal on‐board survey in Bangkok. A passenger waiting‐time survey conducted as part of this study revealed that passengers perceive waiting‐time at bus stops to be greater than actually experienced. The other aim of this study is to examine the potential benefits of bus‐holding using an AVL technology, in terms of waiting‐time, through minimizing bus bunching under different congestion levels. The results are obtained using PARAMICS, and reveal a significant reduction in average waiting‐time.     

Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders

In order to attract more choice riders, transit service must not only have a high level of service in terms of frequency and travel time but also must be reliable. Although transit agencies continuously work to improve on-time performance, such efforts often come at a substantial cost. One inexpensive way to combat the perception of unreliability from the user perspective is real-time transit information. The OneBusAway transit traveler information system provides real-time next bus countdown information for riders of King County Metro via website, telephone, text-messaging, and smart phone applications. Although previous studies have looked at traveler response to real-time information, few have addressed real-time information via devices other than public display signs. For this study, researchers observed riders arriving at Seattle-area bus stops to measure their wait time while asking a series of questions, including how long they perceived that they had waited.The study found that for riders without real-time information, perceived wait time is greater than measured wait time. However, riders using real-time information do not perceive their wait time to be longer than their measured wait time. This is substantiated by the typical wait times that riders report. Real-time information users say that their average wait time is 7.5 min versus 9.9 min for those using traditional arrival information, a difference of about 30%. A model to predict the perceived wait time of bus riders was developed, with significant variables that include the measured wait time, an indicator variable for real-time information, an indicator variable for PM peak period, the bus frequency in buses per hour, and a self-reported typical aggravation level. The addition of real-time information decreases the perceived wait time by 0.7 min (about 13%).A critical finding of the study is that mobile real-time information reduces not only the perceived wait time, but also the actual wait time experienced by customers. Real-time information users in the study wait almost 2 min less than those arriving using traditional schedule information. Mobile real-time information has the ability to improve the experience of transit riders by making the information available to them before they reach the stop.     

An improved headway-based holding strategy for bus transit

Abstract

This paper presents an improved headway-based holding strategy integrating bus transit travel and dwelling time prediction. A support vector machine-based (SVM) model is developed to predict the baseline travel and dwell times of buses based on recent data. In order to reduce prediction errors, an adaptive algorithm is used together with real-time bus operational information and estimated baseline times from SVM models. The objective of the improved holding strategy is to minimize the total waiting times of passengers at the current stop and at successive stops. Considering the time-varying features of bus running, a ‘forgetting factor’ is introduced to weight the most recent data and reduce the disturbance from unexpected incidents. Finally, the improved holding strategy proposed in this study is illustrated using the microscopic simulation model Paramics and some conclusions are drawn.     

Estimating bus run times for new limited-stop service using archived AVL and APC data

In recent years, several transit agencies have been trying to be more competitive with the automobile to attract choice riders. Transit agencies can only be competitive if they can provide services that are reliable, have a short access and egress time, and have run times that are comparable to automobiles. Several transit agencies try to be competitive through offering faster service, such as limited-stop (express) bus service. This study uses AVL and APC data, in addition to a disaggregate data obtained from a travel behavior survey, to select stops and estimate run times for a new limited-stop service that will run parallel to a heavily used bus route (67 Saint-Michel) in Montréal, Canada. Three different scenarios are developed based on theory and practice to select stops to be incorporated in the new limited service. The time savings for each scenario are then evaluated as a range and a fourth scenario is developed. A limited-stop service is recommended based on selecting stops serving both directions of the route, major activity points and stop spacing. This study shows that implementing a limited-stop service would yield substantial time savings for both, the new limited service and the existing regular service running in parallel.     

Not All Transfers Are Created Equal: Towards a Framework Relating Transfer Connectivity to Travel Behaviour

Abstract

Walking from origins to transit stops, transferring between transit lines and walking from transit stops to destinations—all add to the burden of transit travel, sometimes to a very large degree. Transfers in particular can be stressful and/or time‐consuming for travellers, discouraging transit use. As such, transit facilities that reduce the burdens of walking, waiting and transferring can substantially increase transit system efficacy and use. In this paper, we argue that transit planning research on transit stops and stations, and transit planning practice frequently lack a clear conceptual framework relating transit waits and transfers with what we know about travel behaviour. Therefore, we draw on the concepts of transfer penalties and value of time in the travel behaviour/economics literature to develop a framework that situates transfer penalties within the total travel generalized costs of a transit trip. For example, value of time is important in relating actual time of waiting and walking to the perceived time of travel. We also draw on research to classify factors most important to users’ perspectives and travel behaviour—transfer costs, time scheduling and five transfer facility attributes: (1) access, (2) connection and reliability, (3) information, (4) amenities, and (5) security and safety. Using this framework, we seek to explicitly relate improvements of transfer stops/stations with components of transfer penalties and changes in travel behaviour (through a reduction in transfer penalties). We conclude that the employment of such a framework can help practitioners better apply the most effective improvements to transit stops and transfer facilities.     

An application-oriented model of passenger waiting time based on bus departure time intervals

This paper develops an application-oriented model to estimate waiting times as a function of bus departure time intervals. Bus stops are classified into Type A and B depending on whether they are connected with urban rail transit systems. Distributions of passenger arrival rates are analyzed based on field data for Beijing. The results indicate that the best fits for the distribution of passenger arrival rates for Type A and B bus stops are the lognormal distribution and gamma distribution, respectively. By analyzing relationships between passenger arrival rates and bus departure time intervals, it is demonstrated that parameters of the passenger arrival rate distribution can be expressed by the average and coefficient of variation of bus departure time intervals in functional relationships. The validation shows that the model provides a reliable estimation of the average passenger waiting time based on readily available bus departure time intervals.     

Analytical model for calibrating delay at congested bus stops

Abstract

A model is proposed to calculate the overall operating and delay times spent at bus stops due to passenger boarding and alighting and the time lost to queuing caused by bus stop saturation. A formula for line demand at each stop and the interaction between the buses themselves is proposed and applied to different bus stops depending on the number of available berths. The application of this model has quantified significant operational delays suffered by users and operator due to consecutive bus arrival at stops, even with flows below bus stop capacity.     

Six reasons why supply‐oriented indicators systematically overestimate service quality in public transport

Supply‐oriented measures of quality lead to a systematic overestimate of quality as experienced by travellers in public transport. An example is a train with an average occupation rate for seats being 50%, where, nevertheless, the occupation rate observed by travellers is much higher when some parts of the trajectory are busy. Similar examples are discussed for waiting times at stops, probabilities of arriving in time, probabilities of getting a connection and walking distances to bus stops. A plea is then made for putting more effort in measuring demand‐oriented quality measures.     

Analyzing urban bus service reliability at the stop, route, and network levels

Improving the reliability of bus service has the potential to increase the attractiveness of public transit to current and prospective riders. An understanding of service reliability is necessary to develop strategies that help transit agencies provide better services. However, few studies have been conducted analyzing bus reliability in the metropolis of China. This paper presents an in-depth analysis of service reliability based on bus operational characteristics in Beijing. Three performance parameters, punctuality index based on routes (PIR), deviation index based on stops (DIS), and evenness index based on stops (EIS), are proposed for the evaluation of bus service reliability. Reliability involves routes, stops, punctuality, deviation, and evenness. The relationship among the three parameters is discussed using a numerical example. Subsequently, through a sampling survey of bus lines in Beijing, service reliability at the stop, route, and network levels are estimated. The effects of route length, headway, the distance from the stop to the origin terminal, and the use of exclusive bus lanes are also analyzed. The results indicate low service reliability for buses in Beijing and a high correlation between service reliability and route length, headway, distance from the stop to the origin terminal, and the provision of exclusive bus lanes.     

Analysis of real-time control strategies in a corridor with multiple bus services

Control strategies have been widely used in the literature to counteract the effects of bus bunching in passenger‘s waiting times and its variability. These strategies have only been studied for the case of a single bus line in a corridor. However, in many real cases this assumption does not hold. Indeed, there are many transit corridors with multiple bus lines interacting, and this interaction affects the efficiency of the implemented control mechanism.This work develops an optimization model capable of executing a control scheme based on holding strategy for a corridor with multiple bus lines.We analyzed the benefits in the level of service of the public transport system when considering a central operator who wants to maximize the level of service for users of all the bus lines, versus scenarios where each bus line operates independently. A simulation was carried out considering two medium frequency bus lines that serve a set of stops and where these two bus lines coexist in a given subset of stops. In the simulation we compared the existence of a central operator, using the optimization model we developed, against the independent operation of each line.In the simulations the central operator showed a greater reduction in the overall waiting time of the passengers of 55% compared to a no control scenario. It also provided a balanced load of the buses along the corridor, and a lower variability of the bus headways in the subset of stops where the lines coexist, thus obtaining better reliability for all types of passengers present in the public transport system.     

Examining the Political and Practical Reality of Bus-based Real Time Passenger Information

Abstract

Enhancing the bus experience through improved information provision is a key element of UK Government transport policy. Real time passenger information (RTPI) is perceived to reassure waiting passengers, to benefit the bus operator through increased revenue and the local authority, by promoting social inclusion and achieving a modal shift. RTPI also provides an important tool for operators by allowing them to monitor services and refine their schedules.

The aim of this paper is to understand the reasons for implementing RTPI in the bus sector, and to determine the key issues impacting on the likely success of such a policy. A case study approach investigates the experiences of two provincial towns in the UK. The paper suggests that, whilst it is unclear whether RTPI has resulted in an increase in bus patronage, it is considered to be most effective when combined as part of a package of measures. It is intended that the findings from the two case studies will reveal lessons of relevance to authorities contemplating the introduction of RTPI.     

Comparing static and dynamic threshold based control strategies

This research extends a static threshold based control strategy used to control headway variation to a dynamic threshold based control strategy. In the static strategy, buses are controlled by setting a threshold value that holds buses at a control point for a certain amount of time before allowing the bus to continue along the route. The threshold remains constant each time the bus stops at the control point. The dynamic strategy involves the same principle of holding buses at a bus stop; however, a different threshold value is chosen each time the bus holds at a control point. The results indicate that in cases where the static threshold is set equal to the scheduled headway, very low headway variation and passenger system times result; however, passengers on board the bus are penalized by extra delay on the bus while waiting at the control point. The dynamic strategy reduces the penalty to passengers delayed on-board the bus at a control point at the expense of a slight increase in overall passenger system time.The results indicate that in most cases, the tradeoff of the slight increase in waiting time for the significant decrease in on-board delay penalty makes the dynamic strategy an acceptable choice.     

Analysis of light rail rider travel behavior: Impacts of individual,built environment,and crime characteristics on transit access

This paper analyzes factors that influence the mode choice for trips between home and light rail stations, an often neglected part of a person’s trip making behavior. This is important for transit planning, demand modeling, and transit oriented development. Using transit survey data describing St. Louis MetroLink riders in the United States, this study found that some of the factors associated with increased shares of walking relative to other modes were full-time student status, higher income transit riders, and trips made during the evening. It was also found that crime at stations had an impact. In particular, crime made female transit riders more likely to be picked-up/dropped-off at the station. Females are more likely to be picked-up or dropped-off at night. Bus availability and convenience showed that transit riders that have a direct bus connection to a light rail station were more likely to use the bus. Private vehicle availability was strongly associated with increased probability of drive and park, when connecting to light rail.     

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.     

A Modeling Framework for Bus Rapid Transit Operations Evaluation and Service Planning

Abstract

In this paper, we present a dynamic traffic assignment-simulation modeling framework (DYNASMART-P) to support the evaluation and planning of Bus Rapid Transit (BRT) services in urban transportation networks. The model represents the different characteristics associated with BRT operations such as: exclusive right-of-way lanes, limited-stop service, signal prioritization at congested intersections, and enhanced bus stops to reduce passenger boarding times. A set of simulation experiments is conducted using the model to study the impact of introducing a hypothetical BRT service in the Knoxville area in the State of Tennessee. In these experiments, the different operational characteristics of BRT are evaluated in terms of potential impact on transit ridership and on the interacting auto traffic. The results illustrate the advantages of BRT for increasing transit ridership and improving overall system performance.     

Modeling schedule recovery processes in transit operations for bus arrival time prediction

Many existing algorithms for bus arrival time prediction assume that buses travel at free‐flow speed in the absence of congestion. As a result, delay incurred at one stop would propagate to downstream stops at the same magnitude. In reality, skilled bus operators often constantly adjust their speeds to keep their bus on schedule. This paper formulates a Markov chain model for bus arrival time prediction that explicitly captures the behavior of bus operators in actively pursuing schedule recovery. The model exhibits some desirable properties in capturing the schedule recovery process. It guarantees provision of the schedule information if the probability of recovering from the current schedule deviation is sufficiently high. The proposed model can be embedded into a transit arrival time estimation model for transit information systems that use both real‐time and schedule information. It also has the potential to be used as a decision support tool to determine when dynamic or static information should be used.     

An analytical model to conduct a person-based evaluation of transit preferential treatments on signalized arterials

The complexities of urban transportation networks where multiple modes with different characteristics and needs travel in combination with constraints on space and funding make the sustainable management of these systems a challenge. In order to improve transit service, space (e.g., dedicated bus lanes) and time (e.g., transit signal priority strategies) Transit Preferential Treatments (TPT) are deployed to improve transit operations. The objective of this paper is to develop an analytical model that allows for a person-based evaluation of alternative TPTs when considered individually and in combination. In particular, the analytical model is developed to assess person delay and person discharge flow at any intersection that is part of a signalized arterial, where auto arrivals are in platoons. The performance of TPTs is evaluated using both the analytical model and through microsimulation tests on two intersections of San Pablo Avenue in Berkeley, CA. Space TPTs such as dedicated bus lanes and queue jumper lanes are beneficial in reducing bus person delay when provided in addition to the existing lanes; however, the effectiveness of time TPTs such as green extension depends on the level of auto demand in combination with signal settings. Changes in person discharge flow are not significant for any of the treatments tested with the exception of the bus lane substitution with and without green extension, which led to a significant decrease in person discharge flow. Increased bus frequency increases the effectiveness of transit signal priority in reducing total and bus person delay. The analytical model results produce ranking outcomes that are comparable with the microsimulation ones and therefore, the model may be used for a quantitative evaluation of TPTs without the need for data intensive and time consuming calibration efforts required for microsimulation models. The developed model can be used to guide infrastructure and investment decisions on where such TPTs should be implemented and under what conditions space TPTs should be combined with time TPTs to improve person mobility.     

Integrated optimization of bus priority operations in connected vehicle environment

Most previous works associated with transit signal priority merely focus on the optimization of signal timings, ignoring both bus speed and dwell time at bus stops. This paper presents a novel approach to optimize the holding time at bus stops, signal timings, and bus speed to provide priority to buses at isolated intersections. The objective of the proposed model is to minimize the weighted average vehicle delays of the intersection, which includes both bus delay and impact on nearby intersection traffic, ensuring that buses clear these intersections without being stopped by a red light. A set of formulations are developed to explicitly capture the interaction between bus speed, bus holding time, and transit priority signal timings. Experimental analysis is used to show that the proposed model has minimal negative impacts on general traffic and outperforms the no priority, signal priority only, and signal priority with holding control strategies (no bus speed adjustment) in terms of reducing average bus delays and stops. A sensitivity analysis further demonstrates the potential of the proposed approach to be applied to bus priority control systems in real‐time under different traffic demands, bus stop locations, and maximum speed limits. Copyright © 2016 John Wiley & Sons, Ltd.