Real-time Transit Information Research Articles

Impacts of real-time transit information on transit accessibility – A case study

Transit service accessibility improvement is an important topic among transit planners and policymakers. Real-time transit information (RTI) sharing with the users has several benefits, such as lower wait time at the transit stops and higher transit accessibility. Previous studies assessed the benefits of RTI sharing mostly by analyzing the survey data and simulation-based studies. This study used field trip GPS data to quantify the transit stop wait time reduction in the presence of RTI sharing. A smartphone app was developed in this study to disseminate RTI to the users (i.e., location, speed, weather, and road condition information). The trip data showed that the average reduction of transit stop wait time after the RTI use was about three minutes on a selected transit route in Morgantown, WV. The calculated coverage area improvement indicates that the network-level stop coverage can be increased by 37 % with the use of RTI. The reduced wait time at transit stops reduced the average trip time, and riders can walk from further distances to reach the transit stops. The findings of this study justify more widescale use of the RTI sharing in an urban and rural environment.

A Stated Preference Analysis of Real-Time Public Transit Stop Information

This paper presents the results of a study conducted to examine the benefits derived from real-time public transit stop information. This research examines the preferences between several options of receiving real-time public transit stop information. The literature states that one of the main reasons individuals access real-time information is to remove the uncertainty when using public transit. This relationship was examined to ascertain if the provision of real-time public transit information can remove the uncertainty and frustration associated with using public transit.A nested logit model structure was applied in this study to examine the benefits derived from accessing real-time public transit information. These models are estimated on segments of the dataset to ascertain how the mode of transport used by the respondents impacts the benefits derived from using real-time public transit information. The results of this study demonstrate that passengers derive the greatest benefit from accessing transit stop information from real-time information displays. Respondents were shown to obtain the second highest utility when accessing transit stop information via a mobile phone short message service (SMS). Bus users were found to gain the highest benefit from the provision of real-time transit stop information.

Trends in Mobile Transit Information Utilization: An Exploratory Analysis of Transit App in New York City

Smartphone applications that provide transit information are now very popular. However, there is limited research that examines when and where passengers use mobile transit information. The objective of this research was to perform an exploratory analysis of the use of a smartphone application known as Transit App, which provides real-time transit information and trip planning (schedule) functionality. Backend data from Transit App were examined by time of day and day of week in the New York City metropolitan area. The results show that the pattern of both the trip planning feature and overall real-time information usage follow the typical pattern of transit ridership, which has morning and evening peaks. Additionally, self-reported household locations of Transit App users in the New York City area were compared with household socioeconomic characteristics (specifically, income, ethnicity, and age) from census data using GIS visualizations and the Pearson correlation coefficient, but they do not appear to be correlated. This implies that passengers use Transit App regardless of household income, race, or age trends in their neighborhood. This exploratory study examined a rich new data source—backend data from a transit information smartphone application—that could be used in many future analyses to help transit agencies better understand how transit riders use information and plan their trips.

OneBusAway Multi-Region – Rapidly Expanding Mobile Transit Apps to New Cities

Real-time transit information offers many benefits to transit riders, including reduced wait times and increased customer satisfaction. However, offering real-time transit services has been challenging for many transit agencies. While mobile applications (apps) have emerged as a preferred dissemination method for real-time information, it is typically cost-prohibitive for transit agencies to fund custom development of native mobile apps for all popular smartphone platforms. Third-party developers can offer services if an agency openly shares real-time data, but these individuals are volunteers whose priorities and deadlines may not be the same as the agency’s. As a result, few cities have full app portfolios that cover all smartphone platforms. This paper presents the OneBusAway multi-region project, a collaborative effort that is enabling the rapid expansion of native mobile transit apps to new cities. OneBusAway is an open-source transit information system that has provided real-time transit services to the Puget Sound (Washington) area since 2008. The new OneBusAway multi-region feature expands the coverage of the existing Android, iPhone, Windows Phone, and Windows 8 apps for OneBusAway to new cities, including Tampa and Atlanta. The multi-region system architecture, collaborative design and development process, and lessons learned from this ground-breaking project are discussed. The fundamental shift from proprietary to open-source software in the transit industry that has made this type of project possible also is examined.

University Traveler Value of Potential Real-Time Transit Information

Intelligent transportation systems (ITS) have become common in public transit systems, particularly providing real-time transit information. For new implementations, it remains difficult to predict and quantify system and user benefits of technology implementation. Although previous studies have quantified the operational benefits of real-time transit traveler information systems, a gap in knowledge exists around passenger benefits of such systems. The objective of this research was to create a refined method for evaluating transit rider benefits from real-time traveler information and predict changes in traveler behavior. The study was conducted on a rural university campus, isolating the impacts of the system from the multiple influences that often affect transportation in larger metropolitan areas. This study uniquely integrated transit system performance, pedestrian travel times, and traffic simulation to determine travel times and predict mode split. Findings indicated that reducing passenger waiting anxiety was the most significant measure of traveler benefit from such a system. While the benefits found were specific to the study site, the methodology can be used for other transit systems evaluating real-time transit technology investments in rural or urban environments.

Impacts of Real-Time Transit Information on Bus Passengers’ Travel Choices Based on Travel Behaviour Survey

Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to change their travel choice under RTI. When data from the control and experimental groups are compared, the differences in route selection are significant. Specifically, passengers with RTI have a more complex route selection than those without, including their changes of travel mode, departure time, vehicles, and stop choices. The research findings can provide insights into the optimisation of intelligent transit information systems and the strategy of RTI. Also, the analysis of passengers’ travel choice under RTI in the transit network can help to improve network planning.

Solving urban electric transit network problem by integrating Pareto artificial fish swarm algorithm and genetic algorithm

This study presents a multi-objective optimization model for the urban electric transit network problem with the aim of simultaneously designing the layout of bus routes, the frequency and the location and size of charging stations by making a tradeoff between two inconsistent objectives from the perspectives of passengers and operators. A Pareto artificial fish swarm algorithm (PAFSA) embedded with the genetic algorithm (GA) is developed to solve the proposed model. The PAFSA is designed to iteratively search for the proper network configuration satisfying two conflicting objectives. During which, the demand assignment with real-time transit information is performed to update the frequency of each newly designed route. The GA embedded into the PAFSA iteratively decides the locations of charging stations and the number of chargers to be installed in each charging station. A case study of the transit network in an urban region of a city in China is implemented, revealing that the proposed approach is able to rationally design a relatively large-scaled transit network with searching for the best fits between two inconsistent objectives.

Does real-time transit information reduce waiting time? An empirical analysis

Abstract A claimed benefit of real-time information (RTI) apps in public transit systems is the reduction of waiting time by allowing passengers to appropriately time their arrivals at transit stops. Although previous research investigated the overall impact of RTI on waiting time, few studies examine the mechanisms underlying these claims, and variations in its effectiveness over time and space. In this paper, we theorize and validate the sources of RTI-based users’ waiting time penalties: reclaimed delay (bus drivers compensating for being behind schedule) and discontinuity delay (an artifact of the update frequency of RTI). We compare two RTI-based strategies – the greedy strategy used by popular trip planning apps and a prudent strategy with an insurance buffer – with non-RTI benchmarks of arbitrary arrival and following the schedule. Using real-time bus location data from a medium-sized US city, we calculate the empirical waiting times and risk of missing a bus for each trip planning strategy. We find that the best RTI strategy, a prudent tactic with an optimized insurance time buffer, performs roughly the same as the simple, follow-the-schedule tactic that does not use RTI. However, relative performance varies over time and space. Moreover, the greedy tactic in common transit apps is the worst strategy, even worse than showing up at a bus stop arbitrarily. These results suggest limitations on claims that RTI reduces public transit waiting times.

Analyzing network-wide patterns of rail transit delays using Bayesian network learning

Rail transit delays are generally discussed in terms of on-time performance or problems at individual stops. Such stop-scale approaches ignore the fact that delays are also caused and perpetuated by network-wide factors (e.g., bottlenecks caused by shared tracks by multiple transit lines). The objective of this paper is to develop a network model and metrics that can quantify the delay dependencies between transit network stops, and identify local sources of network-wide issues. For this purpose, Bayesian network learning (at the intersection of machine learning and network science) was utilized. Based on the calculated Bayesian networks (BNs), network metrics (inducer and susceptible) were formulated to quantify the network-wide impacts of the delays experienced at the stops. To implement the proposed framework, the delays at Long Island Rail Road (LIRR) were gathered through a crowdsourced real-time transit information app called onTime. The developed BN model was tested through cross-validation, yielded promising accuracy results, successfully identified the problematic stops based on LIRR reports, and provided further insights on network impacts. The BN model and the developed metrics were further tested using a natural experiment, i.e., a before and after study focusing on a recently completed track expansion project at LIRR. The findings imply that BN learning can successfully identify the network dependencies and indicate the rail links/corridors that are the best candidate for subsequent improvement investments. Overall, the developed metrics can quantify the delay dependencies between stops and they can be used by policy makers and practitioners for investment and improvement decisions.

Assessing the barriers to equity in smart mobility systems: A case study of Portland, Oregon

There is an active debate about the potential costs and benefits of emerging “smart mobility” systems, especially in how they will serve communities already facing transportation challenges. This paper describes the results of an assessment of these equity issues in the context of lower-income areas of Portland, Oregon, based on a mixture of quantitative and qualitative research. The study found that by lowering costs and improving service for public transit, ridesharing and active transportation, smart mobility systems could address many of the needs of transportation disadvantaged communities. Similar to those found in other case studies, significant barriers prevent smart mobility technologies from benefiting all communities. For example, lower income survey respondents and respondents of color had significantly lower access to the “smart mobility ecosystem” including bank accounts and credit cards, they rely more heavily on paying cash for transit tickets, had lower access to internet at home and work, and were more likely to reduce data use or cancel cell plans because of cost or data restrictions. Respondents were also concerned about information security, as the impacts of loss or theft, especially identity theft can be devastating for lower-income residents. Since integrating payment systems and relying on internet and cell data for mobile applications is a core feature of smart mobility ecosystem, these disparities are significant barriers to the equitable transition to smart mobility. Policy recommendations to address barriers include expanding free and public WiFi, better real-time transit information, improved training, and language translation for phone applications, among other things.

Transit Information Utilization during an Extreme Weather Event: An Analysis of Smartphone App Data

Extreme weather events such as heavy snow can severely disrupt urban transportation systems. When this occurs, travelers often seek information about the status of transportation services. This study aims to assess information utilization during an extreme weather event by analyzing data from a smartphone application (“app”) called Transit, which provides real-time transit and shared mobility information in many cities. This research focuses on a snowstorm that hit the northeastern United States in January 2016 and severely disrupted transit and shared mobility services. An analysis of Transit app data is conducted in four parts for New York City, Philadelphia, and Washington, D.C. First, hourly app utilization during the snowstorm was compared with mean hourly app utilization prior to the storm. Second, the rate of app usage was calculated by dividing hourly utilization during the storm by the mean hourly volume before the storm. Third, an ordinary least squares regression model of hourly app usage was estimated for each city. Last, a feature within the app used to request Uber vehicles was examined. The results of the first three analyses reveal that overall app usage decreased during the snowstorm in all three cities; after the storm, New York experienced a significant increase in overall app use during the first Monday commuting period. The analysis of Uber data reveals that app users continued to search for ridehailing services during the snowstorm, despite travel bans. These findings are important for transportation operators and app developers to understand how travelers use information during extreme weather events.

LRT passengers’ responses to advanced passenger information system (APIS) in case of information inconsistency and train crowding

This research explores and attempts to understand transit riders’ behavioural responses towards real-time transit information for two specific situations: the presence of inconsistent information on transit service recovery and the effects of crowded trains during rush hours. A survey was designed and conducted to collect light rail transit (LRT) riders’ behavioural responses in Calgary, Alberta. Multinomial logit models were developed and calibrated to explore the effects of the described scenarios on riders’ responses. The results led to the conclusion that socioeconomic attributes, experience with advanced passenger information system (APIS) system, familiarity with public transit in general and Calgary’s LRT system in particular, and the characteristics of origin LRT stations had strong influences on travellers’ behavioural responses. It was also determined that travellers’ actions vary significantly depending on the purpose of the trip, time of the trip, and weather conditions.

A literature review of the passenger benefits of real-time transit information

ABSTRACTRecently, it has become common practice for transit operators to provide real-time information (RTI) to passengers about the location or predicted arrival times of transit vehicles. Accompanying this is a growing body of literature that aims to assess the impacts of RTI on transit passenger behaviour and perceptions. The main objective of this research is to compile a literature review of studies that assess the passenger benefits of RTI provision. The results suggest that the primary behavioural changes associated with providing RTI to passengers pertain to decreased wait times, reductions in overall travel time due to changes in path choice, and increased use of transit. RTI may also be associated with increased satisfaction with transit service and increases in the perception of personal security when riding transit. A second objective of this review was to identify areas for future research based on remaining gaps in the literature; two keys areas that were identified are assessing actual behavioural changes of path choice of transit riders and conducting cost–benefit analyses post implementation of RTI systems. The results of this study have immediate implications for public transit operators considering implementation or expansion of RTI systems and researchers seeking topics for future investigation.

Evaluation of Real-Time Transit Information Systems: An information demand and supply approach

Abstract This study assesses current needs in the implementation of Real-Time Transit Information Systems. Web surveys are used to better understand information supply and demand, defined as the attitudes and experiences with real-time information of transit passengers and agencies, respectively. The most valued types of information demanded were found to be related to vehicle location while the least valued information relates to vehicle characteristics, like seating availability. Smartphone applications were found to be the preferred medium for receiving information followed by Internet/websites and dynamic message signs. The surveys also revealed that demographic and socioeconomic status influence preferences for real-time information. The information supply survey found that approximately 70 percent of surveyed agencies currently offer real-time information. The largest constraint to providing or improving Real-Time Transit Information Systems (RTTISs) was found to be funding, followed by staffing needs. A comparison between the survey results found that the information currently being provided by transit agencies is mostly in line with the information most valued by transit passengers. The few differences that exist are generally because agencies do not provide information on the media preferred most by passengers. To address these differences, several suggestions are made to improve the implementation of real-time information. This information can be used to better develop and prioritize investment in real-time information systems.

Effects of real-time transit information services on the patronage behavior of passengers

Previous research regarding customer relationship management has shown that acquiring a new customer costs much more than retaining an existing customer. Thus, from the perspective of transit agencies, maintaining existing passengers is a cost-effective strategy. This paper investigates the patronage behavior of bus passengers when real-time transit information (RTTI) is made available. Survival analysis based on survey data collected in Kaohsiung city, Taiwan, shows that the risk of RTTI users ceasing their use of transit was 32.9% that of RTTI non-users. Moreover, passengers using RTTI tended to use public transportation 0.15 years longer than those who did not use RTTI. This study found that RTTI helps to retain passengers and provides concrete suggestions regarding RTTI promotion and bus business management for administrators.

An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida

Public transit agencies often struggle with service reliability issues; when a bus does not arrive on time, passengers become frustrated and may be less likely to choose transit for future trips. To address reliability issues, transit authorities have begun to provide real-time information (RTI) to riders via mobile and web-enabled devices. The objective of this research is to quantify the benefits of RTI provided to bus riders. The method used is a behavioral experiment with a before–after control group design in which RTI is only provided to the experimental group. Web-based surveys are used to measure behavior, feeling, and satisfaction changes of bus riders in Tampa, Florida over a study period of approximately three months.The results show that the primary benefits associated with providing RTI to passengers pertain to waiting at the bus stop. Analysis of “usual” wait times revealed a significantly larger decrease (nearly 2min) for RTI users compared to the control group. Additionally, RTI users had significant decreases in levels of anxiety and frustration when waiting for the bus compared to the control group. Similarly, they had significant increases in levels of satisfaction with the time they spend waiting for the bus and how often the bus arrives at the stop on time. Taken together, these findings provide strong evidence that RTI significantly improves the passenger experience of waiting for the bus, which is notoriously one of the most disliked elements of transit trips. The frequency of bus trips and bus-to-bus transfers were also evaluated during the study period, but there were no significant differences between the experimental and control groups. This is not surprising since the majority of bus riders in Tampa are transit-dependent and lack other transportation alternatives.The primary contribution of this research is a comprehensive evaluation of the passenger benefits of RTI conducted in a controlled environment. Moreover, this research has immediate implications for public transit agencies – particularly those serving largely transit-dependent populations – facing pressure to improve service under tight budget constraints.

Analyzing the Behavior of Smartphone Service Users

This paper reports the findings of a study into the behavior of the users of a mobile service. The study analyses the behavior of travelers using a Smartphone application to access real-time transit information and contrasts such user behavior with that of users accessing a transit information service from a website. Previous research in this field has tended to focus upon the perceived benefits of providing real-time transit information and without investigating when and how often passengers would use such real-time transit information services. This paper specifically explores the behavior patterns of travelers using a Smartphone service and those of using a website to provide real-time transit information. Based on empirical data derived from real information services, the impact on user behavior of providing a mobile service is analyzed and contrasted to traditional Web-based service provision. The Smartphone service is furthermore used to conduct a passenger survey to obtain information on the individuals using the mobile service. The results of the analysis presented demonstrate that the demand for information from the website is constant throughout the working week whereas demand for Smartphone information increases during the week peaking during late afternoons and on Fridays. The results of the passenger survey demonstrate that over 80 percent of Smartphone application users are between 18 and 49 years of age and perhaps most importantly, that Smartphone survey questions are twice as likely to be answered compared to the response rates for Web or mail surveys.

Special issue on public transport systems

The Journal of Advanced Transportation has been a leader in the area of public transport for over four decades. This special issue presents an outstanding series of novel papers on public and freight transport systems in the areas of demand estimation, planning, and operations to highlight recent advances. In the following, we paraphrase information from the abstracts of the papers. The first two papers deal with demand estimation issues. In “A method to estimate the historical US air travel demand,” Tao Li, Hojong Baik, and Antonio A. Trani propose a route-based optimization model for estimation of the historical air travel demand in the USA. In the paper “Geographic information system - system dynamics procedure for bus rapid transit ridership estimation” by Luis David Galicia and Ruey Long Cheu, a two-step procedure for estimating the total daily ridership of a new bus rapid transit route is proposed. The two-step procedure has been validated with actual demographic and ridership data from two lines. The proposed procedure offers an approach that complements the ridership estimation methods currently accepted by the US Federal Transit Administration. The third and fourth papers deal with urban railway planning issues. In “A stochastic multi-period investment selection model to optimize strategic railway capacity planning,” Yung-Cheng Lai and Mei-Cheng Shih develop a stochastic multi-period investment selection model to assist railroads to allocate capital investments within the strategic capacity planning process. Their novel optimization framework provides a means to cope with unfulfilled demand and demand uncertainty in a long-term multi-period investment selection problem. Using this decision support tool will minimize the risk in strategic capacity planning, subject to demand uncertainty. In “Development of a transfer-cost-based logit assignment model for the Beijing rail transit network using automated fare collection data,” Bingfeng Si, Ming Zhong, Jianfeng Liu, Ziyou Gao, and Jianjun consider the major factors that influence the passenger flow pattern in the Beijing rail transit network through a logit-based network flow assignment model. A full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty for additional transfers, is proposed to simulate passengers' transfer behavior. They show that the models developed are capable of reproducing transfer and route choices made by passengers. The next three papers deal with issues related to information provided to passengers in urban public transport systems. In “A methodology for schedule-based paths recommendation in multimodal public transportation networks,” David Canca, Alejandro Zarzo, Pedro L. González-R, Eva Barrena, and Encarnación Algaba analyze intermodal itinerary recommendations given to passengers in interurban networks with several public transportation modes. Additionally, time, and capacity constraints, as well as seat booking, are considered. The related optimization problem is modeled for a generic user request and solved with the use of a network graph transformation. In “Users' views on current and future real-time bus information systems,” Md. Matiur Rahman, S.C. Wirasinghe, and Lina Kattan examine the users' views and perceptions towards the possible future availability of real-time bus information systems through a face to face survey. The results showed that 35.5% of the respondents agreed that the current information system discouraged them from using public transport. Of those surveyed, 82% board the first arriving bus regardless of the route, even though it may take a longer in-vehicle time to complete the trip. A majority of the respondents (88%) indicated that real-time transit information would not be necessary if bus headways are less than 10 minutes. Information on the next bus arrival time received the highest priority. In “Railway passenger train delay prediction via neural network model,” Masoud Yaghini, Mohammad M. Khoshraftar, and Masoud Seyedabadi present an artificial neural network model to predict the delay of passenger trains in Iranian Railways. To evaluate the proposed model, they compared the results of three different data input methods as well as three different architectures with each other and with some common prediction methods such as decision tree and multinomial logistic regression. The final paper is on bus transit operations. In “A rule-based model for integrated operation of bus priority signal timings and travelling speed,” Wanjing Ma, Yue Liu, and Baoxin Han study the integrated operation of signal timings and bus speed to provide priority to busses at isolated intersections when real-time adjustment of bus speed is available. A set of integrated operational rules is developed for busses with and without schedule deviation with the objective of minimizing bus schedule deviation, bus fuel consumption, and emissions.

Attitudes of bus operators towards real-time transit information tools

Although it is apparent that providing useful information has a positive effect on transit riders, no studies to date have investigated bus operators’ reactions to real-time arrival information and other potential rider information tools. In this study, the project team surveyed 253 bus operators to determine their views and values concerning the existing use of real-time information and to ask about future transit rider information applications. Almost all operators (93 and 91 % on two separate questions) were positive or neutral to the provision of real-time information. In addition, operators were receptive to building other new information applications, with all applications in the survey being supported by at least 60 % of the bus operators. The two most widely supported potential applications in the survey were additional tools to help blind and deaf-blind riders (89 % of bus operators favored) and an application that would aid riders in identifying physical stop, shelter and bus issues such as graffiti, broken parts or a need for lights (88 % of bus operators). Applications displaying data about past performance or current bus capacity received the least support (66 and 61 % respectively). This research gives a better understanding of the impact of rider information tools on bus operators, including the views and values of the operators, and the harms and benefits of such tools.

Benefits of Real-Time Transit Information and Impacts of Data Accuracy on Rider Experience

When presented in a practical format, real-time transit information can improve sustainable travel methods by enhancing the transit experience. This paper identifies the positive shift realized by the continued development of the OneBusAway set of real-time transit information tools. In addition, the paper analyzes real-time prediction errors and their effects on the rider experience. In 2012, three years after the development of location-aware mobile applications, a survey of current OneBusAway users was conducted to compare the results with the previous 2009 study. Results show significant positive shifts in satisfaction with transit, perceptions of safety, and ridership frequency as a result of the increased use of real-time arrival information. However, this paper also provides a perspective of the margin of error riders come to expect and the negative effects resulting from inaccuracies with the real-time data. Although riders on average will ride less when they have experienced errors, a robust issue-reporting system as well as the resolution of the error can mitigate the initial negative effects. With this understanding, the paper provides transit agencies and developers with guidance to realize the full potential of real-time information and error-reporting systems.