Transit Travel Time Research Articles

Modeling Bus Dwell Time with Decision Tree-Based Methods

A large proportion of transit travel time is made up by dwell time for passengers boarding and alighting. More accurate modeling and estimation of bus dwell time (BDT) can enhance the efficiency and reliability of the public transportation system. Multiple linear regression (MLR) has been the most commonly used method in the literature for modeling and estimating BDT. However, the underlying assumptions of the MLR method, such as multicollinearity and normality of random error, cannot always be satisfied for real applications. This study developed and implemented two methods based on decision trees (DTs), namely, classification and regression tree and chi-squared automatic interaction detector, for the first time for BDT modeling and estimation. The models were compared with the traditional MLR model after calibrating and validating the new models against the data collected from four bus stops in Auckland, New Zealand. Various error measurements were used to evaluate the accuracy of the models. The DT-based methods eliminated the limitations of the MLR method and provided reliable and accurate estimation of BDT.

Estimation of Bus Transport Ridership Accounting Accessibility

Accessibility is defined as a measure of the ease with which an individual can pursue an activity of a desired type, at a desired location, by a desired mode, and at a desired time. It influences the modal choice of a trip maker preferring public transport. Estimation of ridership of a public transport system is to predict the behavioral patterns of individuals and their influence on mode selection. Formulation of a framework for evaluating the accessibility of urban transportation networks and assessing its influence on the ridership of bus transit system is the main focus of the paper.The accessibility measures encompass a range of available opportunities with respect to the attractiveness and travel impedance. In this approach the accessibility measures were considered in geographically aggregated level mainly because of two reasons. One is that this framework is intended for planning decisions which by necessity deals with areas and not with individuals living in it. Another reason is to showcase the methodology of interrelating the accessibility measures and ridership of a transport system. Hence these measures are indispensable to be of plain and easy to understand and work with.The bus route (21G) from Tambaram to Broadway in Chennai city was chosen for this study. It is of 35km in length. Bus passenger opinion was collected at the bus stops of the study route. The questionnaire was prepared in such a way that it records the passengers’ accessible distance, time and cost of trips from their home to destination including transfers. Global Positioning System (GPS) and Electronic Ticketing Machines (ETM) are provided in all the buses operated in the route and these data were collected for a week. The OD pattern of travelers was taken from the Chennai City Traffic Study (CCTS) of Chennai Metropolitan Development Authority and updated with the ETM and passenger opinion survey data. The in-vehicle travel time of the trips and headway of buses at each bus stop were obtained from GPS data. The headway of the trips alludes the waiting time of the passenger at bus stops. The study route was split up into 16 zones. Accessibility Index (AI) of the each zone was determined using Composite Impedance Gravity measure of Urban Accessibility Index tool of TransCAD. Multinomial Logit Model was chosen for aggregation of the accessibility measures in 2 dimensions viz. Time and Purpose. The AIs of the zones were presented in graphical form to show in which areas the accessibility of the public transportation is to be improved. A model was developed to relate the boarding and alighting of passengers at each bus stop with that of the AIs using Multiple Linear Regression.The various parameters of accessibility measures were determined and specific indices to represent the accessibility level of the transit corridor pertaining to key factors influencing the trip production and attraction are presented. It is demonstrated in the paper that how this approach could be developed into a decision supporting tool to assist in a range of transport and land-use planning activities. The impact on ridership for changes in the Accessibility Indices due to variation in bus transit fare and travel time is reported.

RECRUITMENT OF COMMUNITY‐RESIDING YOUTH INTO STUDIES ON AGGRESSION

Recruitment of community‐based youth into studies is challenging. We examined access issues, minority status, and personal costs of participation for a study of children with aggressive behaviors, designed to identify which ones are at risk for future violent behaviors, to identify protective factors, and to test interventions to reduce aggression. Of 1,038 contacts, 112 declined, 239 could not be re‐contacted, and 124 were ineligible. Three hundred and fifty of 563 scheduled child‐parent dyads completed intake assessment. Most were recruited through targeted mailings (33%) and community flyers (22%), 12% through regional news advertisement, 8% by Craigslist, and 5% through healthcare providers/clinics. Factors contributing to enrollment rates by zip code showed the percentage of Black residents per zip code and targeted mailings positively contributed (Beta = .200 & .419, respectively) and estimated transit travel time negatively contributed (Beta =.299) to enrollment rates (R2 = 0.562). Targeted mailings proved to be the most efficient strategy in successful recruitment.

Towards equitable transit: examining transit accessibility and social need in Toronto, Canada, 1996–2006

The location of transit infrastructure distributes publicly-funded benefits to residents throughout a region. However, these benefits are not always distributed equally among different population groups. This research seeks to determine whether the benefits of Toronto’s public transit system are equitably distributed, and how these benefits change from 1996 to 2006 after the implementation of several transit projects in the region. We develop a methodology using a social indicator based on census tract level socio-economic characteristics to measure the relationship between social disadvantage and accessibility to jobs and transit travel time in the Toronto region over time. Transit equity is examined at three levels: spatially, temporally, and by job type. We find the range in accessibility and transit travel time narrows over the 10year period. In addition, the most socially disadvantaged census tracts have statistically significantly better accessibility and lower transit travel times relative to the rest of the region in both 1996 and in 2006. Our findings show that Toronto has a generally equitable transit system that benefits those in social need, who are likely to gain the most from transit. The methodology proposed presents a useful way to bring issues of social equity directly into the land use and transportation planning process.

Influence of Unscheduled Random Public Bus Stops on Transit Travel Time

Transit Travel time can affect to a large extent the service reliability, operating cost, and system efficiency. This research paper aims to study the negative impact of the unscheduled random public bus stops on travel time for a particular bus route in Cairo, Egypt. These unscheduled stops became a usual behavior for Cairo Public buses, which affects more than four and a half million daily users of this transportation service inside Cairo. In this study, a comprehensive research plan was designed to collect the data concerning the bus behavior along a selected bus route, using GPS data logger. The data collection included time, location, speed, unscheduled stops, and scheduled stops. The collected data was then used to develop a trip time model. The developed model revealed the delay time due to the unscheduled bus stops and the scheduled bus stops. The analysis of the data also showed that passengers rely much more on the unscheduled random bus stops than the scheduled bus stops. The study concluded that minimizing the unscheduled bus stops will decrease the trip time, and so improve the service reliability to a large degree. 

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based transit signal priority (TSP) control method is presented in this paper. An online transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan redevelopment feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Predictive Strategy for Transit Signal Priority at Fixed-Time Signalized Intersections: Case Study in Nanjing, China

It is essential to give priority to transit vehicles at signalized intersections in the megacities of China such as Nanjing. This study developed a transit signal priority (TSP) strategy for fixed-time signals with the use of techniques for advanced and continuous vehicle detection and prediction of arrival time. In the proposed model, transit vehicles were traced once they were positioned at one intersection and were advancing to the subject intersection. For instance, the location and the speed of a transit vehicle were measured every few seconds, and the prediction was made simultaneously by a model integrating historic and real-time data. With the predicted arrival time, the length of the transit signal phase would be adjusted to give the green light to the transit vehicle. The proposed strategy was evaluated with VISSIM simulation and compared with conventional TSP strategies (green extension and early green) in a typical roadway and traffic setting of Nanjing. Results indicated that the conventional TSP strategy could hardly result in significant improvement in transit delay and that the proposed strategy significantly reduced the transit travel time. Despite these findings, the adverse impacts on the delay of other road traffic by TSP could not be ignored. Results of the simulation model indicated that the proposed predictive TSP strategy was less disruptive to other traffic and that its performance in nonpeak periods was superior to that in peak periods. The results also suggest that a well-designed algorithm for the prediction of arrival time is the key to success in implementing predictive TSP control.

Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior

Applications of automatic fare collection data were investigated, with a focus on analysis of travel time reliability and estimation of passenger route choice behavior. Beijing Metro was used as a case study. A rail journey was decomposed, and each component was studied with regard to the uncertainties involved. Methods were then designed and validated to infer platform elapsed time (PET) for through stations and platform elapsed time–transfer (PET-Trans) for transfer stations by using smart card transactional data, train schedules, and complementary manual surveys. With this information, the journey time distribution of any path can be established, and methods were proposed for inferring route choice proportions. After data preparation, the methods were applied to two typical origins and destinations from the Beijing Metro. Key values concerning travel time reliability, such as PET, PET-Trans, travelers left behind (unable to board), and path coefficients, were obtained and interpreted in detail. The outcome of this research could facilitate analysis of transit service reliability and passenger flow assignment in daily operations.

Exploring the Value of Traffic Flow Data in Bus Travel Time Prediction

The accurate prediction of transit travel times has a range of applications to benefit operators and passengers. Transit travel time is affected by several factors such as traffic flow and passenger demand, which have to be considered to make accurate predictions. However, previous studies have not considered real world traffic flow variables in their prediction models. This paper develops artificial neural network (ANN) models to predict bus travel time on the basis of a range of input variables including traffic flow data collected from a bus route in Melbourne, Australia. To overcome the drawback of ANNs in determining the effect of each input variable on the independent variable, the paper adopts a regression analysis to determine the important input variables for prediction. The paper examines the value that traffic flow data would make to the prediction accuracy. To this end, two alternative models are developed and the results are compared with those obtained from the traffic flow data–based models. A historical data–based ANN in which temporal variables are substituted with the traffic flow variable and a timetable-based model that traditionally utilizes scheduled travel times are developed. Although the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.

An improved headway-based holding strategy for bus transit

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.

Anxiety-Based Formulation to Estimate Generalized Cost of Transit Travel Time

This paper examines the effect of unreliable transit service on transit user costs with the goal of increasing the accuracy of mode choice models. The concept advanced here is to include explicitly in the formulation of mode choice models the anxiety experienced by passengers when service is unreliable because of late departure or longer-than-expected in-vehicle travel time. This anxiety is modeled as a generalized cost penalty that is added to actual in-vehicle time. The magnitude of the penalty depends on travelers’ assessment of the likelihood of arriving on time at their destination. It is believed that this formulation of anxiety is behaviorally representative. To test the effects of the formulation, a simulation model is generated that quantifies the anxiety component of generalized cost for 10,000 travelers with various aversions to risk for travel between station pairs with different observed reliabilities. Results suggest that primarily for risk-averse travelers, but also for other classes, anxiety may constitute a high percentage of total generalized cost, which may explain many travelers’ unwillingness to choose transit in cases in which deterministic models suggest that they will. Calibrating a model of this type presents substantial challenges. An approach is introduced that is currently being pursued to gather actual anxiety levels as a function of transit travel reliability. The paper concludes with comments on future research directions.

Transit Travel Time Forecasts for Location-based Queries: Implementation and Evaluation

Recently, the market for location-based services (LBS) has been rapidly expanding. As an LBS value-added service, a concierge service provides users with a minimum total cost route using multiple modes. One of the core features in a concierge service is a travel time forecast which undoubtedly affects decisions by users as to the selection of modes, routes, and time of travel. Multimodal travel time forecasts thus become an essential part of providing LBS. Many researchers have endeavored to develop reliable travel time forecasting models. Most of these studies, however, have shown general forecasting performances without carefully evaluating performances of models with actual time data. The aim of this paper is to implement a transit travel time forecasting model and evaluate the performance. The nonparametric regression model, developed in an earlier study has been implemented and evaluated using real-time transit data.

Analysis of Transit Quality of Service and Employment Accessibility for the Greater Chicago, Illinois, Region

A variety of transit decision support tools have been developed in the Chicago, Illinois, metropolitan area in recent years, including the Regional Transportation Asset Management System of the Regional Transit Authority and the Spatial Decision Support System of the Urban Transportation Center at the University of Illinois at Chicago. Although the Chicago metropolitan area has a variety of public transportation services, the quality of service available in an area and the extent to which transit allows area residents to access employment opportunities spread out across the six-county region vary substantially. This paper focuses on a spatial analysis of the variations of local transit service quality indicators as well as a composite regional employment accessibility measure. It explores the quality of the transit system in the Chicago region through a set of supply- and demand-side indicators at the census tract level. The supply-side indicators include a composite index of transit availability and frequency and transit station asset information. The demand-side measures include the computation of the regional employment accessibility index using a gravity model and transit travel times from travel demand models. A series of these indicators is mapped over the Chicago region at the census tract level. The paper also ranks census tracts on the local transit measures, as well as on the regional transit-based employment accessibility measures, and identifies those areas that are well off and worst off in regard to both types of indexes. The paper draws policy conclusions emanating from each of these categories.

Combined Model for Intermodal Networks with Variable Transit Frequencies

The formulation and solution of a combined mode choice and assignment, intermodal network equilibrium model with asymmetric link cost interactions are presented. Automobile, bus, and rail are the modes considered as travel options in the proposed formulation, along with the combined automobile-to-bus and automobile-to-rail intermodal options. Using park-and-ride facilities as transfer points, travelers may switch from automobile to transit. There is a two-way interaction between automobile and bus transit. The presence of transit vehicles on the highway links is registered through a bus-car equivalency factor, and the transit travel time depends on the highway link congestion level. Transit travel times are also subject to congestion at the transit stops because of boarding and alighting. Transit frequencies vary, depending on the level of congestion. The solution algorithm for the variational inequality and the derivation of the descent direction for the diagonalized problem are also presented. A test ne...

Combined Model for Intermodal Networks with Variable Transit Frequencies

The formulation and solution of a combined mode choice and assignment, intermodal network equilibrium model with asymmetric link cost interactions are presented. Automobile, bus, and rail are the modes considered as travel options in the proposed formulation, along with the combined automobile-to-bus and automobile-to-rail intermodal options. Using park-and-ride facilities as transfer points, travelers may switch from automobile to transit. There is a two-way interaction between automobile and bus transit. The presence of transit vehicles on the highway links is registered through a bus–car equivalency factor, and the transit travel time depends on the highway link congestion level. Transit travel times are also subject to congestion at the transit stops because of boarding and alighting. Transit frequencies vary, depending on the level of congestion. The solution algorithm for the variational inequality and the derivation of the descent direction for the diagonalized problem are also presented. A test network is developed, and several tests are presented to show the convergence of the algorithm as well as the changes in the transit level of service due to transit and highway congestion.

Transit Network Design with Variable Demand

This paper shows the iterative approach to solving transit network design problem, particularly with variable transit demand under a given fixed total demand. Although recent studies, which use a simplified combinatorial search approach, showed their capability of building optimal transit networks and handling the complicated transit travel time characteristics, only this iterative approach is believed to properly handle the dynamic characteristics of the relationship between variable transit trip demand and optimal transit network design. Since transit demand depends on the configuration of the transit network and frequencies of the routes, this approach is more desirable for transit network planning than combinatorial approach. The basic approach generates the optimal transit network from the initial network, which requires the shortest in-vehicle travel time, through iterating the assignment procedure and the improvement procedure until there is no more improvement in the network. With variable transit demand, the modal split procedure is added to the basic model to generate the optimal transit network and to estimate transit demand simultaneously. This paper also shows the relationship between optimal transit network design and critical design inputs, such as transit operating speed, total demand size, and transfer penalty. As results of the analysis, synergistic effect of variable transit demand and the optimal transit network are discussed.

Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

An advanced transit signal priority (TSP) control method is presented: it provides priority operation in response to real-time traffic and transit conditions. A high-performance online microscopic simulation model was developed for the purpose of predicting transit travel time along an intersection approach. The proposed method was evaluated through application to a hypothetical intersection with a nearside bus stop. The performance of the proposed method was compared with that of normal signal operation without TSP and a conventional signal priority method. The experimental results indicated that the developed method provided efficient and effective priority operation for both transit vehicles and automobiles. The proposed method significantly reduced transit vehicle delays as well as side-street traffic delay compared with conventional active priority control.

Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

An advanced transit signal priority (TSP) control method is presented: it provides priority operation in response to real-time traffic and transit conditions. A high-performance online microscopic simulation model was developed for the purpose of predicting transit travel time along an intersection approach. The proposed method was evaluated through application to a hypothetical intersection with a nearside bus stop. The performance of the proposed method was compared with that of normal signal operation without TSP and a conventional signal priority method. The experimental results indicated that the developed method provided efficient and effective priority operation for both transit vehicles and automobiles. The proposed method significantly reduced transit vehicle delays as well as side-street traffic delay compared with conventional active priority control.

Estimating Effect of Time-Related Factors on Transit Use with a Large-Scale Mobility Survey and Transit Information

There is a strong interest among policy makers in measures intended to rationalize private car use. However, this requires prior assessment of the likely impact on car and public transport demand associated with each policy option. For example, what is the likely effect of a reduction of travel time by public transport on mode choice? Such effects can be quantified as elasticities, defining the percentage change in a response variable to a 1% change in an independent variable. The aim of this study is to provide a quantitative overview of the impact of travel time, its components, and its derivatives on transit demand. Data were used from a Belgian mobility survey that was supplemented with a database with a calculated public transport trip for each trip in the mobility survey, with the use of search engines and timetables from the public transport companies. Various functional models were applied to derive demand elasticities. An evaluation of these calculated travel times with public transport search engines shows considerable but reasonable differences with observed trips. Travel time ratios, comparing car and transit travel times, and generalized time estimations, weighting walking and waiting times, offer the best approach to identify the relation between travel time and transit use and result in strongly negative elasticities, particularly when captive travelers are separated. The study contributes to the discussion on the effect of travel time on public transport demand not by using a transport model but by using a survey and transit timetable information.

Modeling Transit Trip Time Using Archived Bus Dispatch System Data

Transit travel time and operating speed influence service attractiveness, operating cost, and system efficiency. The objective of this paper is to estimate the values of parameters that affect the total travel time for a particular bus route in Portland, Oregon. The Metropolitan Transportation District of Oregon (TriMet) provides transit service in the three-county Portland metropolitan area. TriMet has implemented a Bus Dispatch System (BDS) as a part of its overall service control and management system. This BDS provides a rich array of archived data that were used in this study to develop the trip time model for the route under study. This trip time model provides heightened understanding of the factors that affect the trip time on the route. The value of the model was revealed when sensitivity analyses were performed using data from the studied route. This analysis concluded that improvements can be readily achieved after understanding the factors that affect total trip time. Finally, some conclusions and recommendations are presented and suggestions for additional research are provided.