Automatic Vehicle Location Research Articles

A Dynamic Choice Model to Estimate the User Cost of Crowding with Large-Scale Transit Data

Abstract Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due to modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on experience-based learning is also formulated. We develop an expectation–maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of 2 months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.

A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data

A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data

Transfer’s monitoring in bus transit services by Automatic Vehicle Location data

Since transfers increase the connectivity of routes, they improve the characteristics of transit networks. Designing and managing transfers are well-investigated issues arising at the tactical and operational level. Conversely, the monitoring phase was rarely faced to verify the consistency between well planned and/or delivered transfers. In this paper, we tailor an innovative methodology for measuring the rate of transfers between two routes by using archived Automatic Vehicle Location (AVL) data. This measurement is performed spatially, at shared and unshared (but reasonably quite close) bus stops, and temporally at each time period. The results are represented by easy-to-read control dashboards. This methodology is tested by about 240,000 AVL real records provided by the local bus operator of Cagliari (Italy) and provides valuable insights into the characterization of transfers.

A Novel Model for Accessibility of Bus Station and Its Application to Bottlenecks Identification: A Case Study in Harbin

Accessibility plays a crucial role in evaluating and optimizing the service quality of public transport systems. Traditionally, bus accessibility research studies mainly focus on the evaluation from the perspective of the aggregate model and analyze the service radius of the site considering the front end of travel. This paper aims to model microlevel bus accessibility calculation and accurate bottleneck station identification according to the opportunity method with AVL (Automatic Vehicle Location) data. The research featured the following control elements: (1) redefining bus accessibility with bus station as the research object; (2) running time between interstation sections is selected as the control index; (3) A new measure index of accessibility. The farthest direct reachable distance is proposed. Moreover, by introducing the quality control algorithm, the study establishes a model of upper and lower limits of accessibility based on the probability index of bilateral norms, which can be employed to identify the bottleneck stations according to the significance level. Harbin City is selected as the case study for methodology verification and shows that the accessibility method is effective and reliable. Additionally, by taking the travel demand in the region as the adjustment parameter of the quality control algorithm, the study demonstrates the ideal accessibility interval of stations and accurately locates all bottlenecks. The study presented here adds to our understanding of accessibility in the construction and management of buses and provides an essential reference for the following optimization.

Inferring Flow Patterns of Subway Passengers Using a Dynamic Logistics Model with Automated Fare Collection and Automated Vehicle Location Data

Inferring Flow Patterns of Subway Passengers Using a Dynamic Logistics Model with Automated Fare Collection and Automated Vehicle Location Data

Inferring Flow Patterns of Subway Passengers Using a Dynamic Logistics Model with Automated Fare Collection and Automated Vehicle Location Data

Inferring Flow Patterns of Subway Passengers Using a Dynamic Logistics Model with Automated Fare Collection and Automated Vehicle Location Data

Statistical estimation in passenger‐to‐train assignment models based on automated data

AbstractWith the rapid development of metro systems, it has become increasingly important to study phenomena such as passenger flow distribution and passenger boarding behavior. It is difficult for existing methods to accurately describe actual situations and to extend to the whole metro system due to the limitations from parameter uncertainties in their mathematical models. In this article, we propose a passenger‐to‐train assignment model to evaluate the probabilities of individual passengers boarding each feasible train for both no‐transfer and one‐transfer situations. This model can be used to understand passenger flows and crowdedness. The input parameters of the model include the probabilities that the passengers take each train and the probability distribution of egress time, which is the time to walk to the tap‐out fare gate after alighting from the train. We present the likelihood method to estimate these parameters based on data from the automatic fare collection and automatic vehicle location systems. This method can construct several nonparametric density estimates without assuming the parametric form of the distribution of egress time. The EM algorithm is used to compute the maximum likelihood estimates. Simulation results indicate that the proposed estimates perform well. By applying our method to real data in Beijing metro system, we can identify different passenger flow patterns between peak and off‐peak hours.

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organisational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customised factors that may help measure and offer practical solutions for decreasing the level of bus drivers' MWL in real-world road driving. Practitioner summary Mental workload is affected by several contributing factors. Depending on the working context, some of these contributing factors have a more significant influence on the level of the experienced MWL. Therefore, the main factors influencing the MWL of BRT and conventional bus drivers were assessed in their real-life environment. Abbreviations: MWL: mental work load; BRT: bus rapid transit; CB: conventional bus; DALI: driving activity load index; NASA-TLX: NASA task load index; SWAT: subjective workload assessment technique; EEG: electroencephalography electrocardiogram; fNIRS: functional magnetic resonance imaging; ITS: intelligent transportation systems; AVL: automated vehicle location

Estimation of transfer walking time distribution in multimodal public transport systems based on smart card data

Transfers are a major contributor to travel time unreliability for journeys in public transport. Thus, connections between services in the public transport network must be reliable. To plan such reliable transfers from e.g. busses to trains, it is crucial to know the necessary walking times from stops to platforms. This paper presents an innovative approach for estimation of walking time distributions from bus stops to train platforms based on a matching of smart card data and automatic vehicle location data. The observed times from bus stop to rail platform turns out to have a large variance, due to two reasons: differences in passenger walking speeds, and passengers who are doing activities during the transfer. To account for these variations a hierarchical Bayesian mixture model is applied, where the time for passengers walking directly and passengers doing activities during the transfer follows separate distributions. The proposed methodology is applied to 129 stations in the Eastern part of Denmark, where the tap-in devices are located at the train platform. Results from two stations with different characteristics are presented in details along with justifications and analyses of model accuracy. The outcome of the model with distributions of the necessary walking times from bus stops to train platforms is important input for timetabling connections, and the data-driven methodology can easily be applied at scale.

Intrapersonal variability in public transport path choice due to changes in service reliability

Uncertainty during the course of a trip is regarded as a particularly disturbing phenomenon by public transport (PT) passengers and has been shown to have a substantial impact on travel behaviour. However, so far there has been little empirical evidence within contemporary research concerning the degree of mid- to long-term adaptation among PT passengers to changes in the principal cause of such uncertainty within PT transport systems: Lack of service departure reliability. While most studies to date have focussed on instantaneous or short-term behavioural responses, this paper presents results from a one-year panel dataset consisting of individuals’ trip itineraries derived from smart card transactions enriched by automatic vehicle location (AVL) data. We measured long-term line route usage and departure reliability, the latter in terms of headway regularity and schedule adherence, in two panel waves. Thus, we aimed for practical indicators that may be associated with specific line routes and origin–destination PT stop pairs used for recurring trips over time. Results from logistic regression indicate a consistent significant impact of changed headway regularity and punctuality change for line routes with relatively high departure frequency. However, there is a relative indifference to long-term reliability change by a significant share of individuals that have actual trade-offs between at least two line route options, possibly indicating satisficing behaviour among PT passengers. Implications on PT path choice forecasting and route planning are discussed.

Effects of rural bus stops on travel time and reliability

Due to relatively low patronage levels, rural bus stops are sometimes questioned in order to improve travel time and reliability on regional bus services. Previous research into stop spacing has focused on urban areas, which means that there is a lack of knowledge regarding the effects of bus stops in regional networks, with longer distances, higher speeds, and lower passenger volumes, in general. The present study addresses this knowledge gap by analysing the effects of bus stops on a regional bus service regarding average travel times, travel time variability, and on-time performance. This is done by statistical analysis of automatic vehicle location (AVL) data, using a combination of methods previously used for analysis of rail traffic and urban bus operations. The results reveal that bus stops that are only used sporadically have a limited impact on average travel times, in general. In contrast, they are all the more influential on travel time variability, and, in turn, on on-time performance. On the studied bus service, the number of stops made have a far greater impact on travel time variability than any of the other included variables, such as the weather or traffic conditions during peak hours. However, the results suggest that rural bus stops have a much lower impact than what we define as secondary bus stops in urban areas. Consequently, by primarily focusing on bus stop consolidation in urban areas, it is possible to significantly improve service reliability without impairing rural coverage.

Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

Estimating the route-level passenger demand profile from bus dwell times

This paper explores the feasibility of estimating the demand profile of a bus route consisting of boarding flows, alighting flows, passenger loads, and origin–destination (OD) flows using dwell times as the main data source. Dwell times can be obtained from Automatic Vehicle Location (AVL) data, or door opening and closing records. This data is often more obtainable than the direct demand information based on passenger counts or fare collection information. A flexible Bayesian framework allowing for various dwell time models is employed to infer the passenger demand profile conditional on the observed bus dwell times. We model passenger flows by stop-specific arrival rates and a conditional alighting probability matrix. A modified gravity model is used to further explain the alighting probability matrix and to reduce the number of unknown parameters. Hamiltonian Monte Carlo sampling is implemented to draw the posterior distribution for the parameters of interest. The methodology is validated by three lines of a bus system where alighting times are the main activity time. For these lines dwell time as well as passenger count data are available. The case study shows that accurate estimates of alighting flows and passenger loads can be derived by the proposed framework with dwell times only but that room remains for improving the accuracy of boarding flows and OD flows. Additional boarding counts of a few bus runs, which can be easily integrated into the observation block of the estimation framework, raise the estimation accuracy of boarding and OD flows to an acceptable level. We therefore conclude that the proposed methodology can help “data-poor” bus operators to overcome demand estimation problems for transit planning and operation.

Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model

Public transport services are often uncertain, causing passengers’ travel times and routes to vary from day to day. However, since door-to-door passenger delays depend on both intended and realised routes, they are difficult to calculate, as opposed to vehicle delays which can be derived directly from the widely available Automated Vehicle Location (AVL) data of the public transport system. In this study we use three months of such historical AVL data to calculate corresponding realised routes and passengers delays in a large-scale, multi-modal transport network by formulating and implementing an adaptive passenger path choice model in an agent-based scenario of Metropolitan Copenhagen with 801,719 daily trips. The proposed model allows analysing five different levels of real-time information provision, ranging from no information at all to global real-time information being available everywhere. The results of more than 258 million (positive or negative) passenger delays show that variability of passengers’ travel time is considerable and much larger than that of the public transport vehicles. It is also shown that obtaining global real-time information at the beginning of the trip reduces passengers delay dramatically, although still being inferior to receiving such along the trip. Additionally, being able to automatically obtain real-time passenger information while walking and being on-board public transport services is found not to lead to considerable improvements compared to acquiring such information manually while waiting at stops, although slight benefits are demonstrated in supplementary models run with pseudo-intelligent vehicle delay forecasting.

An Offline Framework for the Diagnosis of Transfer Reliability Using Automatic Vehicle Location Data

In public transit networks, transfers occur when more than one route is used to connect origins and destinations. Much research has been done to investigate the design and management of transfers at the tactical and operational levels. However, very few studies have investigated the monitoring phase to check a posteriori if transfers are well planned and/or delivered according to archived automatic vehicle location (AVL) data. This article covers this gap by proposing the first offline framework. This framework preprocesses AVL raw data, performs a diagnosis of transfer reliability over all bus stops and time periods, and discloses the most common sources of transfer unreliability. Easy-to-read control dashboards show the viability of this framework on real bus routes with approximately 145,000 AVL data records to make an accurate transfer analysis and possible service adjustments.

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capacity. Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities. The paper proposes a simulation-based optimization (SBO) framework to simultaneously calibrate path choices and train capacity for urban rail systems using AFC and AVL data. The calibration is formulated as an optimization problem with a black-box objective function. Seven algorithms from four branches of SBO solving methods are evaluated. The algorithms are evaluated using an experimental design that includes five scenarios, representing different degrees of path choice randomness and crowding sensitivity. Data from the Hong Kong Mass Transit Railway (MTR) system is used as a case study. The data is used to generate synthetic observations used as “ground truth.” The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. The proposed approach drives large-scale simulation applications for monitoring and planning.

Measuring the Impacts of Everyday Police Proactive Activities: Tackling the Endogeneity Problem

To examine how the practice of daily proactivity affects and responds to changes in crime at micro geographic and temporal scales. Police calls for service and automated vehicle location data from a large suburban jurisdiction were used to create comprehensive measures of police proactivity. Panel data and the generalized method of moments framework were applied to tease out the endogenous relationships between crime and police proactivity and understand the unique impact of proactive patrol and crime upon one another. Daily police proactivity in this locality was highly stable at micro places, although police did intensify their activities very briefly in response to recent changes in crime. In turn, increases in proactive patrol generated immediate increases in crime reporting, followed by fleeting residual deterrent effects that were weaker and less robust. The patterns remained relatively consistent when varying the units of analysis or focusing on hot spots with different profiles of proactivity, but the deterrent effects appeared more sensitive to model specification. Of all measures of proactivity, patrols of medium length and non-traffic enforcement activities were associated with stronger evidence of crime reduction effects. Short-term adjustments in hot spot patrols appear to produce both reporting effects and temporary residual deterrent effects as measured through calls for service and police vehicle location data. Police could potentially enhance and prolong their deterrence by adopting more deliberate strategies with their daily proactive behaviors, including making their proactive activities more targeted and sustained.

The use of the genetic algorithms for optimizing public transport schedules in congested urban areas

Transit operators face with travel time variability issues related to the design of new transit lines or related to the improvement of the performances of existing lines, especially when transit vehicles without exclusive line. Nowadays, the availability of large data through automated monitoring allows more in-depth this phenomenon of variability in timetables to be pointed out with recorded data. The lack of schedule stability confuses the traveling public and thus the degree of dissatisfaction increases. This leads to a decrease of the public transportation users, especially because of those users having travel alternatives to public transportation. The reliability of the travel time and the vehicle operation according to a stable timetable are the most relevant attributes for the users fidelisation. At the same time, it is well known that the demand for public transportation in congested networks has a large variability over space and time. In this paper, we analyse the deviations from the planned (declared and publicly) schedule for a tram line in Bucharest city in different stations, in diferrent times of the day and in different days of the week, in order to build an optimisation model for needed adjustments to the planned schedule. The data are collected with the Automatic Vehicle Location (AVL) system installed on trams’ board. The number of the adjustments solutions is very large and the genetic algorithm is engaged for the optimisation model solving.

Passenger itinerary inference model for congested urban rail networks

The paper develops a data-driven approach for the inference of passenger itineraries in urban heavy rail systems, the Passenger Itinerary Inference Model (PIIM). In light of increasing demand and crowding, the model can be used to assess the impact of near capacity operations on customers, evaluate system performance, and understand passenger behavior when choosing alternative routes. PIIM uses data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems and is applicable to general urban rail networks with entry and exit fare transactions. PIIM consists of three main modules: the left behind model, the route fractions inference model, and the inference model. The left behind model estimates the probability of any passenger being left behind by station and time interval. The route fractions inference model estimates the route choice fractions given the left behind probabilities. The inference model maps each pair of fare transaction records (entry and exit) to a set of feasible itineraries and infers the train(s) boarded by passengers based on the left behind probabilities and the route choice fractions. Synthetic data using AFC and AVL data from a major, congested urban rail system were generated to validate the model. The results show that the model accurately predicts the itineraries used by passengers, the left behind probabilities, route choice fractions, train loads, and other performance metrics of interest. PIIM is also used with actual data to illustrate its applicability.

Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications

Bunching occurs when transit vehicles are unable to maintain their scheduled headways, resulting in two or more vehicles arriving at a stop in close succession and following each other too closely thereafter. Very few studies have explored the prediction of bunching in real-time, particularly for streetcar services. Predicting the time to bunching in real-time allows transit agencies to take more preventive actions to avoid the occurrence of bunching or to minimize its effects. In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models. Based on the findings from the literature review, we propose a model for real-time prediction of streetcar bunching. The Kalman filtering model predicts the travel time to bunching incidents and is tested and analyzed using an automatic vehicle location data feed for one streetcar route (Route 506 Carlton), obtained from the Toronto Transit Commission’s next bus system. The results show that: (1) the model provides good predication quality given that it relies only on the real-time GPS feed of streetcars, which makes it practical for use in real-time prediction applications; (2) the model prediction accuracy improves as the transit vehicle travels away from the terminal; and (3) increasing the number of past days involved in the calculations beyond 6 days or increasing the number of leading trips considered in the same day beyond 7 or 10 trips might increase the prediction error.