Understanding the impacts of a combination of service improvement strategies on bus running time and passenger’s perception

Transit agencies implement many strategies in order to provide an attractive transportation service. This article aims to evaluate the impacts of implementing a combination of strategies, designed to improve the bus transit service, on running time and passenger satisfaction. These strategies include using smart card fare collection, introducing limited-stop bus service, implementing reserved bus lanes, using articulated buses, and implementing transit signal priority (TSP). This study uses stop-level data collected from the Société de transport de Montréal (STM)’s automatic vehicle location (AVL) and automatic passenger count (APC) systems, in Montréal, Canada. The combination of these strategies has lead to a 10.5% decline in running time along the limited stop service compared to the regular service. The regular route running time has increased by 1% on average compared to the initial time period. The study also shows that riders are generally satisfied with the service improvements. They tend to overestimate the savings associated with the implementation of this combination of strategies by 3.5-6.0 min and by 2.5-4.1 min for both the regular route and the limited stop service, respectively. This study helps transit planners and policy makers to better understand the effects of implementing a combination of strategies to improve running time and passenger’s perception of these changes in service.     

Reducing the cost of peak hour transit service through contracting out service

Transit service contracting has responded to fiscal and financial woes of public transit agencies as the most uniquely attractive cost‐saving strategy at present. Most transit service contracting, however, has been in the traditional provision of entire fixed route bus service or commuter express bus service, and exclusive demand responsive service for the general public or for special disadvantaged population groups such as the elderly and/or the handicapped. This paper presents a new module in transit service contracting whereby the public and private operators jointly provide the peak service on the same route and at the same time. While the public agency provides the base demand of the service, the private provider provides the excess demand, both following the same schedules and similar service arrangements. In this paper, proposed service arrangements, costing and contracting procedures are discussed. It is also reported that substantial cost savings ranging from 32 to 57% with an average savings of 48% can be achieved if the excess peak hour bus transit service on highly peaked routes in public transit agencies is contracted to competing private operator(s).     

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.     

Ridership estimation procedure for a transit corridor with new bus rapid transit service

Ridership estimation is a critical step in the planning of a new transit route or change in service. Very often, when a new transit route is introduced, the existing routes will be modified, vehicle capacities changed, or service headways adjusted. This has made ridership forecasts for the new, existing, and modified routes challenging. This paper proposes and demonstrates a procedure that forecasts the ridership of all transit routes along a corridor when a new bus rapid transit (BRT) service is introduced and existing regular bus services are adjusted. The procedure uses demographic data along the corridor, a recent origin–destination survey data, and new and existing transit service features as inputs. It consists of two stages of transit assignment. In the first stage, a transit assignment is performed with the existing transit demand on the proposed BRT and existing bus routes, so that adjustments to the existing bus services can be identified. This transit assignment is performed iteratively until there is no adjustment in transit services. In the second stage, the transit assignment is carried out with the new BRT and adjusted regular bus services, but incorporates a potential growth in ridership because of the new BRT service. The final outputs of the procedure are ridership for all routes and route segments, boarding and alighting volumes at all stops, and a stop‐by‐stop trip matrix. The proposed ridership estimation procedure is applicable to a new BRT route with and without competing regular bus routes and with BRT vehicles traveling in dedicated lanes or in mixed traffic. The application of the proposed procedure is demonstrated via a case study along the Alameda Corridor in El Paso, Texas. Copyright © 2015 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.     

Analyzing transit service reliability using detailed data from automatic vehicular locator systems

The widespread adoption of automated vehicle location (AVL) systems and automatic passenger counters (APCs) in the transit industry has opened new venues in operations and system monitoring. In 2005, Metro Transit, Minnesota, implemented AVL system and partially implemented APC technologies. To date there has been little effort to employ the collected data in evaluating transit performance. This research uses such data to assess performance issues along a cross‐town route in the Metro Transit system. We generate a series of visual and analytical analyses to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level to demonstrate ways of identifying causes of decline in reliability levels. The analytical models show that while headways are maintained, schedule revisions are needed to improve run time and schedule adherence. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends stop consolidation as a tool to decrease variability of service through concentrating passenger demand along a fewer number of stops. Copyright © 2010 John Wiley & Sons, Ltd.     

Modeling and optimizing urban bus transit considering headway variation for cost and service reliability analysis

Due to the stochastic nature of traffic conditions and demand fluctuations, it is a challenging task for operators to maintain reliable services, and passengers often suffer from longer travel times. A failure to consider this issue while planning bus services may lead to undesirable results, such as higher costs and a deterioration in level of service. Considering headway variation at route stops, this paper develops a mathematical model to optimize bus stops and dispatching headways that minimize total cost, consisting of both user and operator costs. A Genetic Algorithm is applied to search for a cost-effective solution in a real-world case study of a bus transit system, which improves service reliability in terms of a reduced coefficient of variation of headway.     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

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.     

Time of day intervals partition for bus schedule using GPS data

Time of day partition of bus operating hours is a prerequisite of bus schedule design. Reasonable partition plan is essential to improve the punctuality and level of service. In most mega cities, bus vehicles have been equipped with global positioning system (GPS) devices, which is convenient for transit agency to monitor bus operations. In this paper, a new algorithm is developed based on GPS data to partition bus operating hours into time of day intervals. Firstly, the impacts of passenger demand and network traffic state on bus operational performance are analyzed. Then bus dwell time at stops and inter-stop travel time, which can be attained based on GPS data, are selected as partition indexes. For buses clustered in the same time-of-day interval, threshold values of differences in dwell time at stops and inter-stop travel time are determined. The buses in the same time-of-day interval should have adjacent dispatching numbers, which is set as a constraint. Consequently, a partition algorithm with three steps is developed. Finally, a bus route in Suzhou China is taken as an example to validate the algorithm. Three partition schemes are given by setting different threshold values for the two partition indexes. The present scheme in practice is compared with the three proposed schemes. To balance the number of ToD intervals and partition precision, a Benefit Evaluation Index is proposed, for a better time-of-day interval plan.     

A comparison of accuracy and cost of attribute data in analysis of transit service areas using GIS

User oriented transit service is designed to meet the particular needs of a selected group of travelers. Transit Routes are located to provide convenient linkages between user's origin and destination in such a way that out-of-vehicle time, such as access and transfer time, is minimized. Planning transit routes requires understanding demographics, land use and travel patterns in an area. The dynamic nature of these systems necessitates regular review and analysis to insure that the transit system continues to meet the needs of the area it serves. Geographic Information Systems (GIS) provide a flexible framework for planning and analyzing transit routes and stops. Socioeconomic, demographic, housing, land use, and traffic data may be modeled in a GIS to identify efficient and effective corridors to locate routes. Part of the route location and analysis problem requires estimating population within the service area of a route. A route's service area is defined using walking distance or travel time. The problem of identifying service areas for park and ride or auto/bus users is not considered here, but assumed analogous to walk/bus trips. This paper investigates the accuracy and costs associated with the use of different attribute data bases to perform service area analysis for transit routes using GIS. A case study is performed for Logan, Utah, where a new fixed route service is operated. The case study illustrates the use of census data, postal data, data collected from aerial photographs, and data collected during a field survey using the network area analysis technique for transit service area analysis. This comparison allows us to describe the amount of error introduced by various spatial modeling techniques of data bases representing a variety of aggregation levels.     

New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas

The percentage of the population being served by a transit system in a metropolitan region is a key system performance measure but depends heavily on the definition of service area. Observing existing service areas can help identify transit system gaps and redundancies. In the public transit industry, buffers at 400 m (0.25 miles) around bus stops and 800 m (0.5 miles) around rail stations are commonly used to identify the area from which most transit users will access the system by foot. This study uses detailed OD survey information to generate service areas that define walking catchment areas around transit services in Montreal, Canada. The 85th percentile walking distance to bus transit service is found to be around 524 m for home-based trip origins, 1,259 m for home-based commuter rail trip origins. Yet these values are found to vary based on our analysis using two statistical models. Walking distances vary based on route and trip qualities (such as type of transit service, transfers and wait time), as well as personal, household, and neighbourhood characteristics. Accordingly, service areas around transit stations should vary based on the service offered and attributes of the people and places served. The generated service areas derived from the generalized statistical model are then used to identify gaps and redundancies at the system and route level using Montreal region as an example. This study can be of benefit to transport engineers and planners trying to maximize transit service coverage in a region while avoiding oversupply of service.     

Stochastic transit equilibrium

We present a transit equilibrium model in which boarding decisions are stochastic. The model incorporates congestion, reflected in higher waiting times at bus stops and increasing in-vehicle travel time. The stochastic behavior of passengers is introduced through a probability for passengers to choose boarding a specific bus of a certain service. The modeling approach generates a stochastic common-lines problem, in which every line has a chance to be chosen by each passenger. The formulation is a generalization of deterministic transit assignment models where passengers are assumed to travel according to shortest hyperpaths. We prove existence of equilibrium in the simplified case of parallel lines (stochastic common-lines problem) and provide a formulation for a more general network problem (stochastic transit equilibrium). The resulting waiting time and network load expressions are validated through simulation. An algorithm to solve the general stochastic transit equilibrium is proposed and applied to a sample network; the algorithm works well and generates consistent results when considering the stochastic nature of the decisions, which motivates the implementation of the methodology on a real-size network case as the next step of this research.     

A simulation of transit bus emissions along an urban corridor: Evaluating changes under various service improvement strategies

This study investigates the impacts of transit improvement strategies on bus emissions along a busy corridor in Montreal, Canada. The local transit provider, Société de Transport de Montréal, has implemented a number of strategies which include the use of smart cards, limited-stop (express bus) service, and reserved bus lanes along this corridor. Using data collected on-board for instantaneous speeds and stop-level ridership, we estimated bus emissions of greenhouse gases and other pollutants at three levels: road segment, bus-stop, and per passenger. A regression of segment-level emissions against a number of explanatory variables reveals that reserved bus lanes and express bus service reduce emissions significantly. On the other hand, smart card use reduces idling emissions compared to other fare payment methods. Our findings are of most relevance for transit planners who are seeking to implement different strategies to reduce emissions and improve transit performance.     

The impact of real-time information on bus ridership in New York City

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.     

Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.     

An actuary method for the estimation and comparison of urban bus transit cost of services

This paper presents a new cost allocation method developed for estimating the fully-allocated costs of the excess peak hour bus transit service provided by two public transit systems. The estimates were produced for the explicit purpose of carrying out a realistic comparison of these costs and the costs of the potential provision of the same service by private operators. The method utilizes the same service data for the analysis. The pragmatic estimation of the fully allocated costs of service by the public and private sectors enables a more accurate estimation of potential cost savings. Sensitivity analysis was also performed using the same costing procedure to determine the range of cost savings that are feasible in a competitive contract arrangement of private sector providers. The new method separates costs of the direct provision of service from other indirect costs and thus facilitates the identification of each cost item and its significance in comparative cost estimates.     

Evaluation of transit operations: data applications of Tri-Met's automated Bus Dispatching System

In this paper, archived Automatic Vehicle Location and Automatic Passenger Counter data are used to evaluate actual bus running time variation in relation to scheduled service for Tri-Met, the transit provider for the Portland, Oregon metropolitan area. Given observed variation in running times, scheduled recovery times are found to be generally (though not universally) excessive. This results in an under-investment of resources in revenue service relative to non-revenue service. Analysis of trip level data reveals that bus operators are an important source of running time variation after controlling for such factors as route design, time of day and direction of service, and passenger activity.     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

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.