Search

Travel time variability (TTV) plays a significant role in analysing the reliability of public transit system. The research works carried out on travel time variability under Indian traffic conditions are very few and these studies did not analyse the performance of travel time distribution in detail, considering different temporal and spatial aggregations. In this study, travel time variability is analysed using travel time distributions considering different temporal and spatial aggregations. The Automatic Vehicle Location (AVL) data of four transit routes of Mysore City, Karnataka, India are used to evaluate travel time distributions with respect to temporal aggregations (peak period, off-peak period, 60 minutes, 30 minutes and 15 minutes) and spatial aggregations (route level and segment level). The performance of travel time distributions is analysed using the Anderson-Darling (AD) test. The segments with signalised intersections and different land-use types are analysed to evaluate the distribution fit for various conditions. The results of both route and segment level analysis report highest accuracy and robustness values for Generalised Extreme Value (GEV) distribution. The distribution is proved to be superior in describing travel time variability of public transit.

In this information era commuters prefer to know a reliable travel time to plan ahead of their journey using both public and private modes. In this direction reliability analysis using the location data of the buses is conducted in two folds in the current work; (i) Reliability analysis of a public transit service at route level, and (ii) Travel time reliability analysis of a route utilizing the location data of the buses. The reliability parameters assessed for public transit service are headway, passenger waiting time, travel speed, and travel time as per the Service Level Benchmarks for Urban Transport by the National Urban Transport Policy, Government of India. And travel time reliability parameters such as Buffer Time Index, Travel Time Index, and Planning Time Index are assessed as per Federal Highway Administration, Department of Transportation, U S. The study is conducted in Tumakuru city, India for a significant bus route in a limited data sources scenario. The results suggest that (i) the Level of Service of the public transit service needs improvement. (ii)around 30% excess of average travel time is needed as buffer time. (iii) more than double the amount of free flow travel time must be planned during peak hours and in the worst case. In the future, the analysis conducted for the route can be extended for citywide performance analysis in both folds. Also, the same method can be applied to cities with similar demographics and traffic-related infrastructure.

The increasing adoption of automatic vehicle location and automatic passenger count technologies by transit agencies produces passenger boarding and alighting count data on a continuous basis. This data provides new opportunities for origin–destination (O–D) flow estimation. However, the state-of-the-art methodologies generated flows within routes and barely considered linked trips. This paper proposes optimization models to identify transfers and approximate network-level O–D flows by: a quadratic integer program (QIP), a feasible rounding procedure for the quadratic convex programming (QCP) relaxation of the QIP, and an integer program (IP). A case study for Ann Arbor-Ypsilanti area in Michigan suggests that: The IP model outperforms the QCP in terms of accuracy and remains tractable from an efficiency standpoint, contrary to the QIP. Its O–D estimation achieves an R-Squared metric of $$95.57\%$$ at the traffic analysis zone level and $$92.39\%$$ at the stop level, compared to the ground-truths inferred from the state-of-the-practice trip-chaining methods.

To address the power interruption and the speed loss caused by neutral sections in ac electrified railway, many ground-charged auto-passing neutral section devices have been proposed recently. For these devices, the accurate positioning of trains is necessary and important. An automatic pantograph location technique based on voltage and current signals is analyzed ideally in our preceding research. However, according to the experimental data in practical railway, the positioning accuracy may be seriously affected by the actual configuration of catenaries and feeders. It is reported in this article that the circulating current in the neutral section loop is the cause to induce the above issue. Thus, the pantograph location method based on the current information of traction network is deeply analyzed, and a mathematical model with consideration of mutual inductance is built, which can explain the mechanism of the circulating current. Furthermore, an adaptive correction method based on experimental tests is proposed to ensure the positioning accuracy. Through the ground-charged auto-passing neutral section device commissioned in the Shenshuo heavy-haul railway in China, the correctness of the theoretical analysis and the effectiveness of the proposed correction method are verified. This article provides an important guideline for the application of this pantograph location method.

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.

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.

Transit riders consistently rate speed and reliability of service as primary drivers of satisfaction, and transit agencies can help retain and grow ridership by improving these components of service. The challenge for transit agency staff is to identify when and where they should focus efforts to improve service quality. Here we propose an approach to data analysis that identifies and isolates specific aspects of service that are limiting speed and reliability. In-vehicle travel time can be decomposed into time spent in motion and time stopped. Time in motion is often dependent on factors common to general traffic, whereas time stopped has some features in common with general traffic (i.e., traffic signals) and some unique to buses (i.e., passenger dwell). Other sources of delay from serving a bus stop include deceleration, acceleration, and signal delay. To improve overall travel time, transit agencies must prioritize interventions that will contribute the most to improving speed and reliability. We used high-resolution automatic vehicle locator data to assign components of speed and reliability within a trip-level “time budget.” We compared typical time budget components across service types, and used the time budget approach to evaluate local service and Rapid bus service operating simultaneously on the same alignment. Results of the delay and variability quantifications suggested particular interventions, as well as the expected size of the resulting effect. With limited resources, the bus time budget approach could aid understanding and prioritization of transit agency efforts to improve speed and reliability.

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

Reliability is understood in public transport as the certainty travellers have regarding the level of service they will experience when travelling. The travel time, waiting time, or the comfort level they will experience inside the vehicle are some of the most important reliability attributes. Reliability is usually neglected from travel behavioural models, since there is a lack of studies addressing its impact in travellers’ choices. This study proposes an in-depth analysis and characterization of travel time reliability in a public transport system using passive data only, which every day is getting more popular worldwide. The study is comprised of two parts, taking Transantiago (the public transport system of Santiago, Chile) as a study case, as it is a good representation of a frequency-based public transport system. In the first part a statistical and graphical analysis of travel times is conducted, focusing on characterizing headway and travel time reliability of public transport services as well as the effect of dedicated infrastructure, based on smartcard transactions and GPS information. In the second part an aggregate mode choice model based on revealed data is developed to analyse the effect of travel time reliability on travellers’ preferences. Overall, this study provides evidence of significant differences among headway variability and in-vehicle travel time dispersion different public transport modes. The standard deviation (a measure of dispersion) can be quite high for bus trips, while in the case of metro is smaller than 4 min independent of trip length. Regarding the aggregate public transport mode choice model, the coefficient of variation of headways is a key attribute to explain modal preferences. It was found that average bus users would accept traveling ~ 5 min longer to completely avoid headway irregularity. All these results were obtained by analysing passive-data from AFC and AVL systems only, which represents a novel approach for choice modelling. Transport modellers should consider the impact of service reliability to improve route choice and passenger assignment models, and better represent users’ perceptions and behaviour.

Emergency Medical Service systems aim to respond to emergency calls in a timely manner and provide prehospital care to patients. This paper addresses the problem of locating multiple types of emergency vehicles to stations while taking into account that vehicles are dispatched to prioritized patients with different health needs. We propose a two-stage stochastic-programming model that determines how to locate two types of ambulances in the first stage and dispatch them to prioritized emergency patients in the second stage after call-arrival scenarios are disclosed. We demonstrate how the base model can be adapted to include nontransport vehicles. A model formulation generalizes the base model to consider probabilistic travel times and general utilities for dispatching ambulances to prioritized patients. We evaluate the benefit of the model using two case studies, a value of the stochastic solution approach, and a simulation analysis. The case study is extended to study how to locate vehicles in the model extension with nontransport vehicles. Stochastic-programming models are computationally challenging for large-scale problem instances, and, therefore, we propose a solution technique based on Benders cuts.

To understand the route choices of public transport users, it is important to know the information available to them, and the context present at that moment. In fact, each choice situation in a transport network has different characteristics and possibilities, also depending on the current status of the transport network. In this regard, travel diaries based on tracking technologies can capture precise observations for a long term. In this work, we exploit a large-scale tracking dataset, collected through a mode detection algorithm, to understand route choices of public transport users. We propose a choice set generation algorithm, able to cover more than 94% of the collected trips without any computational constraint. We compare the users’ paths in the public transport network with different choice sets, under multiple performance indicators, including coverage, size, and fit. This latter is computed by the estimation of a Path Size Logit model. The use of Automatic Vehicle Location (AVL) data allows comparing the available paths in terms of public transport vehicles used. We also consider different information provisions of network conditions and disturbances (full knowledge, no knowledge and current knowledge), and study which information provision best represents the choice set inferred by the observed users’ behaviour. Estimating a Mixed Path Size Logit model, we identified high heterogeneity among the users in only a few aspects. Overall, a condition of no knowledge results as the best fit, i.e. users seem to take into account in a minor way the realized delays in the alternatives considered when deciding their public transport route.

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Throughout the world, major cities are experiencing ever-increasing urbanization and mobility challenges. Current estimates of 55% of the world’s population residing in cities and towns is set to increase to 68% by 2050. An ever-growing population is adding much pressure on existing infrastructure, forcing city leaders to look towards technologies such as “Internet of Things” to aid in improving municipal quality of life, transforming cities into “Smart Cities”. An important subset of Smart Cities are the connected vehicles operating within its proximity. Aggregated vehicle data can provide “data blanketing” of every street throughout the city, while monitoring vehicle parameters such as vehicle speed, RPM, idle time, fuel usage, location and CO2 emissions. Gathering such data is referred to as Vehicle Telematics, and can be obtained through On-Board Units and AVL (Automatic Vehicle Location) devices. This work aims to compare and evaluate two well-known standards for extracting this vehicle information. Both OBDII and FMS standards are widely used for fleet management and monitoring driving behavior. Through statistical analysis, we propose two algorithms for monitoring instantaneous fuel economy via OBDII. The objective of this research is to determine the best standard for retrieving vehicle sensor data to use when monitoring fuel economy, CO2 emissions, and other attributes that may affect quality of life in Smart Cities.

The rapid advancements in sensor technologies has resulted in the increased use of Automatic Vehicle Location (AVL) systems for traffic data collection. Global Position System (GPS) sensors are the...

Public transport agencies implement different strategies aimed at improving the operation of transit service and to increase satisfaction among its riders. One service strategy employed by transit agencies is a limited-stop bus service that runs in parallel to a heavily used route to decrease travel times for existing riders and to reduce pressure on the local route. Using bus operations data obtained from automatic vehicle location (AVL) and automatic passenger counter (APC) systems and customer satisfaction data collected in Vancouver, Canada, the present study evaluates levels of satisfaction among users of a local and limited-stop bus service while controlling for the service characteristics these users have experienced in the past seven days. Our results reveal that after controlling for characteristics related to the conditions of the service experienced by users, namely passenger activity levels, patrons of the express route service were more likely to be satisfied with the transit service compared to users of the local service. This finding indicates that the operational characteristics of a limited-stop service, including in-vehicle time savings and higher route frequency, are highly valued by its users. Results of this study demonstrate how operations data can provide a greater context for customer satisfaction analyses. Finally, this study provides transit planners and policy makers with a better understanding of how customers perceive local and limited-stop service.

Transit-oriented travel mode is considered as one of the most effective travel strategies for improving the level of travel service and reducing travel time and delay. Curbside bus stops are often built to support the operation of the transit system, which leads to new bottlenecks of road segments near such stops and exerts a negative impact on traffic flows of neighbouring intersections. The authors propose a traffic signal optimisation model with the objective to minimise total travel delay by considering the interaction of traffic flows between the intersections and upstream curbside bus stops. First, transits' following and lane-changing behaviours are explored by analysing their trajectories collected by unmanned vehicle aerial video detectors. Second, transits' occupancy time at curbside bus stops is statistically analysed by using automatic vehicle location data. They then use an equivalent-flow analysis method to quantify the impact caused by transits' actions of lane-changing and moving into/out stop basins (including the waiting process). Such impact is integrated into the algorithm of intersection traffic signal optimisation. They finally validate the proposed signal optimisation model based on real-time traffic data collected from Nanjing, China. It is revealed that the improved model can reduce vehicle travel delay by 4.3% at the tested intersection.

Among the many ways to improve a transit system is a reduction in travel time as experienced by the passenger. Hence, passenger waiting times remain a topic of interest among transit planners. In this study, the effects of transit vehicle delays on passenger waiting time is investigated, as well as the effects of transfer status, boarding location, time of day, and rider travel frequency. The data used in this study were collected using automatic fare collection (AFC) and automatic vehicle location (AVL) technology. A trip chaining algorithm is used to infer the trajectory of each passenger, and as a result produce measures of passenger waiting time and vehicle delay. An analysis of an arterial Bus Rapid Transit (aBRT) line in Saint Paul, Minnesota reveals a waiting time model consistent with previous literature, a positive relationship between vehicle delay and passenger waiting time, and an insignificant relationship between transfer status and passenger waiting time. Finally, a simple model relating waiting time and vehicle delay is provided for the purpose of transit planning and waiting time estimation.

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public...

Transit service reliability is a measure of the quality of service offered by the public transit systems. The nature and pattern of the travel time variability can be described by the travel time distribution and it is the prerequisite in the reliability analysis. Many research works have been carried out to understand the travel time distribution, but there are very few studies on the heterogeneous traffic in developing countries like India. In this study, an attempt has been made to analyze the travel time distribution at different spatial and temporal aggregation scales using Automatic Vehicle Location (AVL) data of public bus route in Mysore city. The results suggest that, Generalised extreme value (GEV) distribution is the better fit for travel times at both route and segment levels and the performance of the Generalised extreme value (GEV) distribution is comparatively better in both temporal and spatial aggregation compared to other distributions. This distribution can be utilized by the transit operators in the operation and performance evaluation of transit systems.

How to collect the real-time information of unconnected vehicles has been a challenge for connected vehicle technologies. The LiDAR sensors deployed along the roadside and at intersections provide a solution to fill the data gap during the transition from the traditional traffic to the full connected traffic. The roadside LiDAR sensors can record the movement of all road users with a relative long detection range. The lane detection serves as a fundamental but important step for LiDAR data processing. The location (which lane is occupied) of vehicles can be used for lane changing detection, vehicle departure warning and wrong-way alerts. But currently, there is not an effective method to identify the boundary of lanes using roadside LiDAR sensors. This paper presents a systematic procedure for lane detection based on the trajectories of vehicles collected on the road with the roadside LiDAR sensor. The whole procedure includes two major parts: background filtering and road boundary identification. This robust procedure can release engineers from the manual lane identification task and can avoid any error caused by manual work. Two case studies confirmed the effectiveness of the proposed method. Compared to previous lane detection methods, this procedure is not affected by the existence of pedestrians. This method can also detect the boundaries of lanes from the roads having curves with the limited time cost.

Providing expected time of arrival (ETA) information to passengers/operators with the help of the latest technologies is reported as a way to improve the effectiveness of the public transport system. There are several solutions to generate such information using automatic vehicle location data. Along with developing solutions, it is also equally important to ensure that delivered information is accurate and would be trusted by the passengers. However, most of the transport agencies which implemented such solutions fail because of the lack of proper evaluation methodology for the solution provided to them. The present study addresses this challenge and reports the development of a systematic evaluation methodology for passenger information systems. A prediction algorithm that was developed using the concepts of classification schemes and filtering techniques was considered as a sample solution. Performance evaluation of algorithms was carried out at various levels such as routes, trips, and bus stops in terms of prediction error (difference between actual arrival time and predicted arrival time to a given bus stop). Prediction accuracy was checked at all bus stops from 30 min ahead onwards. The prediction error thresholds are organized into different bins depending on the prediction horizon. Finally, prediction errors are visualized in a novel way using a parallel coordinate plot that is developed in Java interface. A unique feature of this algorithm is that one can visualize errors at multiple levels and can add multiple attributes at a time. The developed methodology can help operators to test how improvements to the prediction algorithm are impacting the predictions that are seen by passengers.

Weather has the largest collective effect on transport systems, affecting users of all modes at the same time over a wide area. The aim of the research is to use Automatic Vehicle Location System (AVLS) data to investigate the impact of adverse weather, using rainfall, temperature and wind speed as indicators, on bus journey time. It will also assess if the impacts are dissimilar across three bus route types: bus lanes on the entire route, bus lanes on part of the route and buses running in mixed traffic. Previous research tends to use bus ridership levels as the key variable of interest when exploring weather impacts whereas the approach taken here will examine bus journey times. Multiple linear regression and in-depth analyses are used to explore which weather variables have the greatest effects. Results indicate that more bus journey time variation can be explained by weather on weekdays than on weekends. Increases in rainfall, wind speed and temperature resulted in longer journey times, but the extent depends on the season and route type. The results will be useful to bus operators and local authorities in preparing mitigation strategies and for longer term planning, particularly in the context of climate change.