Passenger Count Data Research Articles

Bus Load Inference and Crowding Performance Evaluation through Disaggregate Analysis of Fare Transaction, Vehicle Location, and Passenger Count Data

Comfort is an important aspect of the transit passenger experience. Crowding can significantly decrease passenger comfort and disrupt service delivery, causing passenger travel times to increase and even resulting in passengers being unable to board an arriving vehicle. This research explores the use of automatically collected vehicle location data, fare transaction data, and passenger origin–destination inference to measure crowding on buses. Three model components are involved: scaling vehicle trip-level origin–destination transfer data, measuring crowding as perceived by passengers through performance measures defined for this purpose, and determining the sources of crowding. The latter is important to identify the most effective means of addressing crowding in each case. The models are tested on data from the Massachusetts Bay Transportation Authority, and examples of graphical applications already being used by planners are presented.

Understanding Human Perception of Bus Fullness: An Empirical Study of Crowdsourced Fullness Ratings and Automatic Passenger Count Data

Bus fullness, namely bus crowding level, is one of the most critical metrics used to quantify public transportation service quality and consumer satisfaction. Often it is simply represented by the head count within a bus vehicle that is measured using the prevailing automatic passenger counter (APC)., Little is known, however, about how the precise passenger count reflects the human perception of bus fullness. This study examines the linkage between APC data and crowdsourced fullness ratings data from riders in Pittsburgh, U.S.A., to understand how riders’ perception of bus fullness is related to spatial, temporal, and demographic factors in addition to the passenger counts. We found that human perception of bus fullness, matched with passenger counts, can vary substantially by time of day, bus vehicle seat capacity, and passengers’ income. Last but not least, we proposed a statistical model that estimates riders’ perception of bus fullness based on APC data and bus route characteristics. This model can be used by transit agencies who possess APC data to assess service quality better, and ultimately to enhance the design and operation of transit systems.

Public transport experienced service reliability: Integrating travel time and travel conditions

The paper proposes a generalization of public transport service reliability, incorporating both travel times and travel conditions based on passengers’ perceived journey time. Time is partitioned into waiting and transfer time as well as in-vehicle time under different travel conditions (crowding and seat availability), which may vary along a journey and between days. The experienced service reliability gap (ESRG) index is introduced, defined as the difference between an upper percentile (e.g., the 95th) and the median perceived journey time across days for a particular OD pair and departure time. The metric is evaluated by tracing virtual trips from origin to destination with journey times and travel conditions based on automated vehicle location (AVL) and automated passenger count (APC) data and seated status modelled probabilistically. A study of a high-frequency bus line in Stockholm, Sweden shows that travel conditions co-vary only weakly with nominal journey time, and the ESRG index display patterns across the day not evident in existing reliability measures, such as a wider and later afternoon peak. The ESRG displays significant variation between OD pairs along the line. Correlation with headway variability suggests that measures improving bus regularity have additional positive effects on experienced service reliability.

Harnessing the Potential of Automated Data to Simulate Emissions of an Interregional Bus Route in Toronto, Canada

This study made use of automated vehicle location and automated passenger counter data to simulate the greenhouse gas (GHG) emissions of an interregional bus route in Toronto, Ontario, Canada. The authors analyzed bus performance and emissions as well as quantified emissions under the effects of operational improvements (increasing speed and reducing idling) and different fuels (conventional diesel, compressed natural gas, and biodiesel). Average total trip emissions were 54 kg per bus, with emissions higher in the morning peak period than in the afternoon peak period. Emissions rates on the highway portion of the corridor were lower than emissions rates for the arterial portions, with mean values of 1,627 g/km and 1,993 g/km, respectively. The authors observed that the addition of each passenger influenced bus emissions per passenger differently; when the bus was less crowded, each additional passenger could decrease emissions per passenger by 7%, but that reduction becomes 1.3% when the bus is crowded. Finally, the study results estimated that operational improvements could reduce emissions by 22%, whereas switching to compressed natural gas without speed improvements could reduce emissions by 6%. The effects of emissions reduction strategies are highly dependent on the characteristics of the bus and drive cycle. These results are useful to transit planners in the selection of appropriate GHG reduction strategies as well as in the selection of candidate corridors (highway versus arterial routes) for fleet renewal.

Use of Mobile Device Wireless Signals to Determine Transit Route-Level Passenger Origin–Destination Flows: Methodology and Empirical Evaluation

Directly observing stop-to-stop origin–destination (O-D) flows for transit passengers by onboard surveys is time-consuming and costly. While the use of automatic passenger count (APC) and automatic fare collection data to infer transit O-D flows has undergone important developments lately, the value of directly observing O-D movements remains of interest in light of inference errors. The increasing use of personal mobile devices and the corresponding reliance on wireless fidelity (Wi-Fi) communications in public spaces allow for the prospect of using the detected anonymous Wi-Fi signals of mobile devices carried by transit passengers to extract O-D movements. An empirical study is conducted when O-D movements determined directly from Wi-Fi signal data and from a combination of Wi-Fi and APC data are compared to extensive, directly observed high-fidelity ground truth data obtained from an onboard survey. While O-D flows determined directly from the Wi-Fi data for a specific bus trip do not correspond well to the ground truth flows, the results demonstrate the promise of using Wi-Fi signal data for O-D flow determination when the data are aggregated across multiple bus trips for a time-of-day period, especially when used in conjunction with APC data.

Perspectives on Transit: Potential Benefits of Visualizing Transit Data

Advancements in information and communication technologies have enabled transit agencies around the world to generate streams of data on a high-frequency basis. Increasingly, these agencies are interested in new methods of visualizing these data to communicate the results of their planning efforts, operational investments, and overall transit performance to decision makers and stakeholders. Most agencies today collect and provide numerous kinds of data, including Google’s general transit feed specification schedule data, automatic vehicle location data, and automatic passenger count data. This paper aims to demonstrate the untapped potential of these data sources; specifically, the paper uses transit data from Montreal, Quebec, Canada, to generate performance measures that are of interest to both transit planners and marketing professionals. Some of these measures can also help in communicating the positive attributes of public transportation to the community. Performance measures are generated at different scales, including transit system, neighborhood, route, and stop levels. This paper expands on previous research on transit performance research and visualization by adopting currently available resources for so-called big data.

Method for Assessing Bus Delay in Mixed Traffic to Identify Transit Priority Improvement Locations in Cambridge, Massachusetts

Urban transit services face a number of challenges from space constraints, congestion, and delays, among other issues. Implementing bus priority at traffic signals or providing exclusive operating space for buses can increase the attractiveness of taking the bus and thereby encourage ridership. The City of Cambridge, Massachusetts, was looking to pilot such interventions to demonstrate benefits of bus ridership, but needed a prioritized list of route segments with the largest levels of excess travel time to do so. Three metrics were used to evaluate delay: vehicle delay, overall passenger delay, and system reliability. These three metrics were combined into a single composite rating system for each segment and used to identify route segments along which buses experienced the most delay. The City of Cambridge analyzed high-ridership bus routes with automatic passenger counter data to identify segments along the route where buses experienced substantial delay. The next step for this project is to conduct an on-site field visit of targeted segments to develop potential bus prioritization proposals for each. This paper outlines the method developed to calculate bus delay by segment and presents results for one route analyzed.

Exploring Applications of Second-Generation Archived Transit Data for Estimating Performance Measures and Arterial Travel Speeds

Travel time and operating speed influence the attractiveness, operating cost, and system efficiency of transit service. As part of its bus dispatch system, the Tri-County Metropolitan Transportation District of Oregon has been archiving automatic vehicle location (AVL) and automatic passenger count data for all bus trips at the stop level since 1997. In 2013, a new and higher-resolution bus AVL data collection system was implemented. This new system provides stop-level data as well as 5-s resolution (5-SR) bus position data between stops. The objective of this paper is to explore potential applications of the new data for assessing transit performance and for estimating transportation system performance measures for urban arterials. Results suggest that the 5-SR data provide high-resolution information on time and position that can be used to determine bus travel speeds between bus stops, identify speed breakdowns, and estimate intersection signal and queuing delays.

All aboard at all Doors

The time that buses spend waiting for passengers to board can be a significant portion of a bus route's overall running time. A key determinant of boarding time is the number of doors through which passengers are permitted to board. Transit agencies that allow boarding through all doors, instead of just through the front door, typically enjoy decreased boarding times and decreased running times. This study focused on the feasibility of an all-door boarding policy for La Société de transport de Montréal (STM), the public transit agency of Montreal, Canada. The potential benefits of such a policy were assessed through three main steps. First, a selection methodology was developed to determine which of STM's bus routes would benefit most from various all-door boarding strategies. Second, a multivariate regression analysis was implemented with STM's archived automatic vehicle location and automatic passenger counter data to estimate the dwell and running-time savings that would result under various implementation scenarios. Third, a sensitivity analysis was developed to demonstrate the savings associated with implementing the policy. The findings showed that all-door boardings could yield substantial savings in running time, with morning peak savings as much as 15.8% on the best routes. In many cases, the running-time savings were enough to remove a bus from a route while still maintaining existing frequencies. The findings from this research may be beneficial for transit planners and operators since the presented methodologies show substantial savings from all-door boarding and can be adopted by other transit agencies.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Implementation of an iterative headway-based bus holding strategy with real-time information

In high frequency bus service lines buses often come irregularly at the stops, often in bunches, due to the uncertainty of the passenger demand and behavior and unexpected conditions on the roads. Vehicle holding is a commonly used strategy among a variety of control strategies in transit operation in order to reduce bus bunching and regulate bus headways. This paper investigates a control strategy of holding a group of buses at a single or multiple control point(s). By incorporating any passenger boarding activities during holding, a single control point problem is developed and extended to multiple control points to reduce the variance of headways for the downstream stops. The problem is a non-convex optimization problem with linear constraints that minimizes the total passenger waiting time both on-board and at stops. A heuristic is then developed that is easy and fast to implement, which makes it suitable for real-time implementation. The model is evaluated with a simulation case study by using the real-time bus operation data (i.e., Automatic Vehicle Location and Automatic Passenger Count Data) from the Chicago Transit Authority (CTA). The simulation results show that considering the boarding activities in the total waiting time, our model mitigates the error propagation and maintains steady performance, compared to the common models in the literature, which do not consider boarding while holding.

Grouping of Bus Stops for Aggregation of Route-Level Passenger Origin–Destination Flow Matrices

Route-level, bus passenger origin–destination (O-D) matrices summarize useful information on travel patterns for use in route planning, design, and operations. However, the size of stop-to-stop O-D matrices makes it difficult to synthesize important flow patterns and to estimate stop-to-stop O-D passenger flows accurately. To reduce the size of the O-D matrix for improved estimation, analysis, and communication, stops can be grouped so that passenger flows between far fewer stop group pairs can be estimated and represented. The problem of grouping of bus stops for aggregation of passenger O-D flows is presented with the explicit objective of reducing the passenger O-D flow matrix while capturing important O-D flow characteristics. Two computationally efficient heuristic algorithms for solution of this problem are proposed. A set of empirical studies—conducted with automatic passenger counter data collected on major bus routes operated by the Ohio State University's Campus Area Bus System, the Central Ohio Transit Authority, and the Los Angeles County, California, Metropolitan Transit Authority—shows that the stop group configurations identified with the proposed heuristic algorithms are close to optimal and capture pertinent flow patterns and dominant clusters of stops much better than stop group configurations produced from land use characteristics. The heuristic algorithms can identify solutions for all possible numbers of stops with a reasonable computational time, whereas optimal configurations can be found for only a few groups on long routes.

Simulating the Travel Time Impact of Missed Transit Connections

It is well established that transit passengers dislike transferring, in part because of the inherent risk that the connecting vehicle will be missed, a risk that can increase overall travel time. Despite the problems that missed transfers cause, such transfers across a system are rarely tracked in transit performance monitoring programs. The likelihood of a missed transfer depends on combinations of several factors and thus is hard to estimate. In practice, transit systems are most often evaluated according to the performance of individual vehicles, stops, and routes, not the interactions between them. This paper describes a systems approach to quantify the effects of travel time reliability, schedule adherence, and schedule design on missed transit connections, and the resulting travel time distributions. To determine the effects of vehicle interactions on transfers and the role that transfers play in travel time, a series of simulations based on automatic passenger counting data from the bus system in San Diego, California, was performed. Travel times on two transfer trips in downtown San Diego were simulated. The effects of passenger arrival rate, on-time vehicle performance, and schedule design on the likelihood of a transfer being missed were investigated in a sensitivity analysis. This research is expected to lead to a better understanding of the passenger effects of schedule adherence on transfer trips. Practically speaking, this methodology could aid in the identification of pairs of buses whose chronic schedule deviations at a particular location are likely causing missed transfers.

Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns from Automatic Passenger Counter Data

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.

Introduction of Reserved Bus Lane

This paper evaluates the impact of adding a reserved bus lane on the running times and on-time performance of two parallel bus routes, one of them a limited-stop bus service and the other a regular bus service. By means of automatic vehicle location and automatic passenger count data, statistical models were built to estimate running time and on-time performance. The reserved bus lane yielded savings of 1.3% to 2.2% in total running time, and benefits were more significant for northbound afternoon peak trips than for southbound morning peak trips because of congestion levels northbound. The introduction of a reserved lane increased the odds of being on time by 65% for both routes. A decline in the variability of running time and delay at the end was noticed after implementation of the reserved lane; the decline indicated that the reliability of the service being offered along the corridor had improved. The analysis showed that the more affected a bus service was by adverse traffic conditions, the more it benefitted in running time from improvements introduced by reserved lanes while keeping schedules constant. Reserved lanes had a substantial effect on both service reliability and on-time performance, two key variables in customer satisfaction that justified such implementation. This study will help transit planners and schedulers to understand the effects of implementing reserved lanes on running time, on-time performance, and transit schedules.

Limited-stop bus service: an evaluation of an implementation strategy

Transit agencies implement limited-stop (express) bus services as a mean to provide an attractive and competitive transit service. The Societe de transport de Montreal (STM), the transit agency providing service on the Island of Montreal, Canada, implemented a limited-stop service to run parallel to route 67 Saint-Michel, a heavily used bus route. The design process regarding which stops to include in the new service involved an evaluation of several scenarios and an estimation of running time savings. In this paper, we use archived vehicle location and passenger count data to measure changes in on-time performance and running times, and to compare these changes to previous estimates through a before-and-after study. In addition, an on-site survey is conducted to measure the riders’ perception of time savings. Implementing a limited-stop service yielded to 4.6 minutes savings in running time for the new limited service; meanwhile, the existing regular service experienced an increase of 0.8 minute in average running time, an increase that could be due to a new smart card system introduced in the same period. The running time savings for the limited-stop service fall in the expected estimates made by the research team, which strengthens our trust in the methodology used to select stops and to estimate savings. Finally, the study shows that riders are generally satisfied with the new service. They also tend to overestimate the savings associated with the implementation of the new limited-stop service by 4 to 7 minutes more than the actual savings.

Identifying Causes of Performance Issues in Bus Schedule Adherence with Automatic Vehicle Location and Passenger Count Data

Automatic vehicle location (AVL) and automatic passenger counting (APC) systems can provide rich archived databases for analysis. Previous work has focused on using AVL–APC data to evaluate system performance using various quantitative performance measures and data visualization methods. Given the large volume of data, there is a benefit to automating the creation of performance measures and data visualizations and “pushing” interesting information to users, rather than requiring users to create the performance measures and figures and sift through them on their own. This paper presents a methodology for identifying bus stops that are not meeting performance standards for schedule adherence and the factors that cause inadequate performance. The methodology is designed to be automated and therefore can be applied efficiently to AVL–APC data for an entire transit network. Use of this proposed method will enable transit agencies to identify service quality issues and their root causes more efficiently.

Analysis of the Unrealized Demand for Bus Transportation, Based on Passenger Counter Data

Analysis of the Unrealized Demand for Bus Transportation, Based on Passenger Counter Data

A comparative study of aggregate and disaggregate gravity models using Seoul metropolitan subway trip data

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.