Bus travel time modelling using GPS probe and smart card data: A probabilistic approach considering link travel time and station dwell time

Abstract

Path travel time estimation for buses is critical to public transit operation and passenger information system. State-of-the-art methods for estimating path travel time are usually focused on single vehicle with a limited number of road segments, thereby neglecting the interaction among multiple buses, boarding behavior, and traffic flow. This study models path travel time for buses considering link travel time and station dwell time. First, we fit link travel time to shifted lognormal distributions as in previous studies. Then, we propose a probabilistic model to capture interactions among buses in the bus bay as a first-in-first-out queue, with every bus sharing the same set of behaviors: queuing to enter the bus bay, loading/unloading passengers, and merging into traffic flow on the main road. Finally, path travel time distribution is estimated by statistically summarizing link travel time distributions and station dwell time distributions. The path travel time of a bus line in Hangzhou is analyzed to validate the effectiveness of the proposed model. Results show that the model-based estimated path travel time distribution resembles the observed distribution well. Based on the calculation of path travel time, link travel time reliability is identified as the main factor affecting path travel time reliability.     

A hybrid model based method for bus travel time estimation

Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.     

A framework for travel time variability analysis using urban traffic incident data

This study aims to develop a framework to estimate travel time variability caused by traffic incidents using integrated traffic, road geometry, incident, and weather data. We develop a series of robust regression models based on the data from a stretch in California's highway system during a two-year period. The models estimate highway clearance time and percent changes in speed for both downstream and upstream sections of the incident bottleneck. The results indicate that highway shoulder and lane width factor adversely impact downstream highway clearance time. Next, travel time variability is estimated based on the proposed speed change models. The results of the split-sample validation show the effectiveness of the proposed models in estimating the travel time variability. Application of the model is examined using a micro-simulation, which demonstrates that equipping travelers with the estimated travel time variability in case of an incident can improve the total travel time by almost 60%. The contribution of this research is to bring several datasets together, which can be advantageous to Traffic Incident Management.     

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

Analyzing travel time reliability and its influential factors of emergency vehicles with generalized extreme value theory

Travel time reliability is very critical for emergency vehicle (EV) service and operation. The travel time characteristics of EVs are quite different from those of ordinary vehicles (OVs). Although EVs own highest road privilege, they may still experience unexpected delay that results in massive loss to the society. In this study, we employ the generalized extreme value (GEV) theory to measure extremely prolonged travel time and analyze the potential influential factors. First, among three GEV distributions, Weibull distributions are found to be the best distribution model according to several goodness-of-fit tests; a new reliability index is derived to measure travel time reliability. Numerical examples demonstrate the advantages of GEV-based reliability index over variance and percentile value in the applications of EV. This index will be of great practicability in the EV operation performance and reliable route choices. Second, we further investigate the potentially influential factors of EV travel time reliability. Results show that link length and left-turn traffic volume may have an adverse impact on the link reliability while more left-turn lanes may increase the travel time reliability. The influential factor study will help us understand the causes of the EV travel time delay and the differences of travel time reliability between OVs and EVs.     

An extended generalized filter algorithm for urban expressway traffic time estimation based on heterogeneous data

Travel time estimation and its variation for urban expressways are vital to both the information provision to road users, and the system evaluation and management for traffic administrators. Fruitful research efforts have been made to develop methodologies of reconstructing spatiotemporal traffic states mainly for freeways based on one or multiple data sources. However, few studies specifically focused on urban expressways. There are more intensive merging and diverging traffic due to short distances between ramps, for example, 300–500 m. Based on the empirical analysis of traffic data collected on a typical segment of a congested urban expressway, this study proposes an extended generalized filter algorithm for the urban expressway traffic state estimation based on heterogeneous data. More specifically, the multiple sources of data include both fixed sensor data (e.g., inductive loops or radar data) and global positioning system (GPS) probe vehicle data. This study compares the proposed algorithm and the traditional algorithm for freeways using data collected on the segment of expressway in Beijing, China. Results demonstrate the advantage of the proposed method, as well as its feasibility and effectiveness.     

Real-time queue length estimation using event-based advance detector data

Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated by applying the commonly used shockwave model. Although breakpoints can be accurately identified using lane-by-lane detection, few studies have investigated queue length estimation using single-channel detection, which is a common detection scheme for actuated signal control. In this study, a breakpoint misidentification checking process and two input-output models (upstream-based and local-based) are proposed to address the overestimation and short queue length estimation problems of breakpoint-based models. These procedures are integrated with a typical breakpoint-based model framework and queue-over-detector identification process. The proposed framework was evaluated using field-collected event-based data along Speedway Boulevard in Tucson, Arizona. Significant improvements in maximum queue length estimates were achieved using the proposed method compared to the breakpoint-based model, with mean absolute errors of 35.7 and 105.6 ft., respectively.     

基于地图匹配的城市道路实时交通状态模糊综合判别方法

依托于浮动车数据,基于地图匹配对城市道路交通状态模糊综合判别方法进行深入研究.首先根据浮动车数据特点和道路交通信息,基于Mapbasic编程对数据进行地图匹配,并进行MapInfo二次开发,通过相关模型计算指定时段内的道路交通参数.建立模糊综合评价判别模型,对判别结果量化处理,以最大隶属度原则确定道路交通状态.最后,选...