Commuter ride-sharing using topology-based vehicle trajectory clustering: Methodology,application and impact evaluation

This paper illustrates a ride matching method for commuting trips based on clustering trajectories, and a modeling and simulation framework with ride-sharing behaviors to illustrate its potential impact. It proposes data mining solutions to reduce traffic demand and encourage more environment-friendly behaviors. The main contribution is a new data-driven ride-matching method, which tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters. Compared with prevalent carpooling algorithms, which allow users to enter departure and destination information for on-demand trips, the proposed method focuses more on regular commuting trips. The potential effectiveness of the approach is evaluated using a traffic simulation-assignment framework with ride-sharing participation using the routes suggested by our algorithm. Two types of ride-sharing participation scenarios, with and without carpooling information, are considered. A case study with the Chicago tested is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns.     

Multi-objective signal control of urban junctions – Framework and a London case study

This paper presents an approach to multi-objective signal control using fuzzy logic. The signal control uses fuzzy logic where the membership functions are optimised according to the Bellman–Zadeh principle of fuzzy decision-making. This approach is both practical for the decision-maker and efficient, as it leads directly to a Pareto-optimal solution. Signal control priorities are ultimately a political decision. Therefore the tool developed in this research allows the traffic engineer to balance the objectives easily by setting acceptability and unacceptability thresholds for each objective. Particular attention is given in the example to pedestrian delays. The membership functions of the fuzzy logic are optimised by a genetic algorithm coupled to the VISSIM microscopic traffic simulator. The concept is illustrated with a case study of the Marylebone Road–Baker Street intersection in London at which pedestrians as well as vehicle flows are high. The results prove the feasibility of the framework and show the vehicle delays for a more pedestrian friendly signal control strategy.     

Computer vision approach for the classification of bike type (motorized versus non‐motorized) during busy traffic in the city of Shanghai

This article describes a novel approach for the binary classification of two‐wheeler road users in a dense mixed traffic intersection. The classification is a supervised procedure to differentiate between motorized and non‐motorized (human‐powered) bikes. Road users were first detected and tracked using object recognition methods. Classification features were then selected from the collected trajectories. The features include maximum speed, cadence frequency in addition to acceleration‐based parameters. Experiments were conducted on a video data set from Shanghai, China, where cyclists as well as motorcycles tend to share the main road facilities. A sensitivity analysis was performed to assess the quality of the selected features in improving the accuracy of the classification. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 93%. This research contributes to the literature of automated data collection and can benefit the applications in many transportation‐related fields such as shared space facility planning, simulation models for two‐wheelers, and behavior analysis and road safety studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Winter problems on mountain passes – Implications for cost-benefit analysis

Cost-benefit analysis is a tool in government decision-making for determining the consequences of alternative uses of society’s scarce resources. Such a systematic process of comparing benefits and costs was adopted in early years for transportation projects and it has been the subject of much refining over the years. There are still some flaws, however, in the application of the method. In this article we have studied the impact of weather conditions on traffic speed on low traffic roads often exposed to adverse weather. This is an issue not currently considered in the cost-benefit analysis of road projects. By using two analytical approaches—structural equation modelling and classification and regression tree analysis—the impact of the weather indicators temperature, wind speed, and precipitation on traffic speed has been quantified. The data relates to three winter months on the European Route 6 road over the mountain pass Saltfjellet in Norway. Increase in wind speed, increase in precipitation and temperatures around freezing point all caused significant decrease in traffic speed in the case studied. If actions were taken to reduce the impact of adverse weather on traffic (e.g. by building a tunnel through the mountain) this study indicates that the road users would gain a total benefit of approximately 2,348,000 NOK (282,000 EUR) each winter at Saltfjellet if all the weather related benefits were included. We argue that this is a significant number that is highly relevant to include in CBAs. This applies both to the CBAs of new transportation projects as well as when resources are allocated for operation, maintenance, and monitoring of the existing transport systems. Including the weather related benefits would improve the application of CBA as a decision-making tool for policy makers.     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

Evaluation of driver satisfaction of travel information on variable message signs using fuzzy aggregation

Driver satisfaction regarding travel information provided by variable message signs (VMS), which are part of the Nam‐Mountain Tunnel ATIS, was evaluated using fuzzy aggregation. Application of fuzzy aggregation to analyze driver satisfaction allows one to represent the variability and complexity of human perception with great fidelity. A fuzzy weighted average using two sets of fuzzy membership functions was applied to evaluate individual satisfactions of delay and travel time information provided. Then, those individual satisfactions were aggregated to estimate the driver group's overall satisfaction. The evaluated overall satisfaction was 0.65 for delay information and 0.63 for travel time information. Through these results, it was found that users of the travel information provided by the VMS in the Nam‐Mountain Tunnel ATIS were somewhat satisfied with the service quality. Those overall satisfactions were compared with a conventional weighted average and traffic operational effects to demonstrate the usefulness of the developed fuzzy method.     

Developing evasive action‐based indicators for identifying pedestrian conflicts in less organized traffic environments

There has been a growing interest in using surrogate safety measures such as traffic conflicts to analyse road safety from a broader perspective than collision data alone. This growing interest has been aided by recent advances in automated video‐based traffic conflict analysis. The automation enables accurate calculation of various conflict indicators such as time‐to‐collision and post‐encroachment time. These indicators rely on road users getting within specific temporal and spatial proximity from each other and therefore assume that proximity is a surrogate for conflict severity. However, this assumption may not be valid in many driving environments where close interactions between road users are common. The objective of this paper is to investigate the applicability of time proximity conflict indicators for evaluating pedestrian safety in less‐organized traffic environments with a high mix of road users. Several alternative behavioural conflict indicators based on detecting pedestrian evasive actions are recommended to better measure traffic conflicts in such traffic environments. These indicators represent variations in the spatio‐temporal gait parameters (step length, step frequency and walk ratio) immediately before the conflict point. A highly congested shared intersection in Shanghai, China, with frequent pedestrian conflicts is used as a case study. Traffic conflicts are analysed with the use of automated video‐based analysis techniques. The results showed that evasive action‐based indicators have higher potential to identify pedestrian conflicts and measure their severity in high mix less organized traffic environments than time proximity measures such as time‐to‐collision and post‐encroachment time. Copyright © 2016 John Wiley & Sons, Ltd.     

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.     

基于分布式计算机的智能相机在城市交通中的研究与应用

我国逐步开始将城市建设与互联网技术进行高度融合以打造智能城市。智能城市建设过程中,智能交通是重要一环,当前我国交通视频监控系统普遍是通过道路两旁的摄像头拍摄获得路况图像信息,这种方式虽然是解决了一些问题,但是不能满足智能城市的建设需求。本文总结和研究了分布式智能相机的一些关键技术,得出嵌入式设备高效率、低耗能的优点使其在交通监控领域得到了越来越广泛的应用,基于分布式计算机设计了智能相机的分布式智能城市交通系统,提出了一系列有益于提升系统运行的具有应用前景的算法,用来解决使用重叠视野进行跟踪和稀疏相机网络中的有效跟踪。     

Welfare effects of road pricing and traffic information under alternative ownership regimes

This paper models strategic interactions between a road supplier, a provider of traffic information, and road users, with stochastic travel times. Using a game-theoretical analysis of suppliers’ pricing strategies, we assess the social welfare effects of traffic information under various ownership regimes. The results show that the distortive welfare effect of monopolistic information pricing appears relatively small. Collusion of the road operator and information provider yields higher social welfare than independent pricing by two firms. The intuition behind this result resembles that behind the welfare effects of double marginalization, but is not exactly the same, as traffic information is not strictly complementary to road use.     

Viability of new road infrastructure with heterogeneous users

This paper explores the importance of heterogeneity in value of time and route choice when assessing the viability of new road infrastructure to alleviate congestion problems. The model incorporates strategic interaction between road operators in a cost-benefit framework and several competitive regimes are considered. It is then employed to establish the financial and socio-economic viability of a congestion pricing demonstration entering Madrid city centre, where road users have to choose between a free but highly congested road and a priced free-flowing road (semi-private regime). A logit estimation is undertaken with information from a questionnaire among road users in the Eastern Madrid area to obtain users’ value of time and of congestion.The tolls obtained generate a traffic reallocation towards the new roadway such that revenues suffice to render the infrastructure socio-economically viable. The private and the low toll regimes generate similar welfare gains that are close to the first best. Yet the former supposes large losses to users. The low toll and the semi-private regimes do not raise such distributional concerns. However, the low toll regime requires a sufficiently high traffic growth rate to make it financially viable; this does not happen for the other competitive regimes.     

Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas

This paper investigates the effects of the provision of traffic information on toll road usage based on a stated preference survey conducted in central Texas. Although many researchers have studied congestion pricing and traffic information dissemination extensively, most of them focused on the effects that these instruments individually produce on transportation system performance. Few studies have been conducted to elaborate on the impacts of traffic information dissemination on toll road utilization. In this study, 716 individuals completed a survey to measure representative public opinions and preferences for toll road usage in support of various traffic information dissemination classified by different modes, contents, and timeliness categories. A nested logit model was developed and estimated to identify the significant attributes of traffic information dissemination, traveler commuting patterns, routing behavior, and demographic characteristics, and analyze their impacts on toll road utilization. The results revealed that the travelers using dynamic message sign systems as their primary mode of receiving traffic information are more likely to choose toll roads. The potential toll road users also indicated their desire to obtain traffic information via internet. Information regarding accident locations, road hazard warnings, and congested roads is frequently sought by travelers. Furthermore, high-quality congested road information dissemination can significantly enhance travelers’ preferences of toll road usage. Specifically the study found that travelers anticipated an average travel time saving of about 11.3 min from better information; this is about 30 % of travelers’ average one-way commuting time. The mean value of the time savings was found to be about $11.82 per hour, close to ½ of the average Austin wage rate. The model specifications and result analyses provide in-depth insights in interpreting travelers’ behavioral tendencies of toll road utilization in support of traffic information. The results are also helpful to shape and develop future transportation toll system and transportation policy.     

User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison

The level of service (LOS) concept in the Highway Capacity Manual has been used as a qualitative measure representing freeway operational conditions for over 35 years. One key element that has not been adequately addressed is how road users perceive LOS. This exploratory research examines road-user perceptions of freeway LOS by presenting study participants with a series of video clips of various traffic conditions (taken from cameras on overpasses to allow a complete view of the traffic stream) and asking them their perceptions of LOS. A random effects ordered probability model is then used to statistically link participant-recorded perceptions of LOS with measurable traffic conditions (speed, density, flow, percentage of trucks, vehicle headways) and participant characteristics. The findings suggest that the Highway Capacity Manual’s use of traffic density as a single performance measure for LOS does not accurately reflect road-user perceptions. The statistical analysis shows that a number of attributes besides traffic density determine public perceptions of LOS and that these perceptions vary depending on both traffic conditions and road-user characteristics.     

Queuing simulation of roadside survey station: Blocked traffic lane

External station travel surveys provide critical inputs to travel demand models. The results from these models are frequently used for statewide planning purposes. Although a roadside survey is very effective in obtaining useful information from road users, its major drawback is the excessive delay that is imposed onto road users particularly on high-volume facilities. In this paper, we used a discrete event simulation to model a blocked traffic lane survey, which is usually conducted for two-lane undivided highways. This type of survey station requires a complete stop of all oncoming traffic. Non-surveyed traffic has no ability to go around and thus has to wait in a queue in order to proceed through the survey station. Road users’ impacts are quantified in terms of delay and queue length while the performance of surveyors is measured by the number of surveys completed per unit time. Sensitivity analyses of simulation inputs reveal that simulation results are fairly insensitive to selected parameters. The results in this study provide a quick and useful guideline that roadside surveyors can use to estimate the road user impacts prior to the survey and to plan the survey procedure accordingly.     

广州市天河区交通拥堵的微循环解决路径研究

快速城市化致使城市交通量急剧增长,交通拥堵问题日益严重。受社会经济等条件限制,已建的成城区难以进行大规模的改扩工程,交通系统的深化急需跟上城市更新的步伐^[1]。本文以广州市天河区天园街道片区为研究对象,利用互联网电子地图,对该片区周边四条主要交通性道路高峰时段拥堵情况进行实时监测。通过为时一个月的监测,了解到在拥堵高峰时段四条交通性道路规模所能承载的交通量远远低于实际承载的交通通行量。结合实地调研,运用交通微循环理论,提出构建一个合理的交通微循环道路网络方案,即开放片区内部路网增强城市交通的毛细血管,增强四条主干道之间的联系实现交通分流。     

Automated vision inspection of rail surface cracks: A double-layer data-driven framework

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

A microscopic analysis of speed deviation impacts on lane-changing behavior

Driving behavior models that capture drivers’ tactical maneuvering decisions in different traffic conditions are essential to microscopic traffic simulation systems. This paper focuses on a parameter that has a great impact on road users’ aggressive overtaking maneuvers and directly affects lane-changing models (an integral part of microscopic traffic simulation models), namely, speed deviation. The objective of this research is to investigate the impacts of speed deviation in terms of performance measures (delay time, network mean speed, and travel time duration) and the number of lane-change maneuvers using the Aimsun traffic simulator. Following calibration of the model for a section of urban highway in Tehran, this paper explores the sensitivity of lane-changing maneuvers during different speed deviations by conducting two types of test. Simulation results show that, by decreasing speed deviation, the number of lane changes reduces remarkably and so network safety increases, thus reducing travel time due to an increase in network mean speed.     

基于DSP的先进视频交通数据采集系统探讨

DSP是一种先进的视频交通数据采集系统,它操作简单,不仅能够有效的融入于道路交通数据采集系统,还能够与感应式十字路口信号灯控制系统、电子警察抓拍系统相结合,其双重功用被广泛应用于城市道路、高速公路交通数据采集,以及公路隧道、桥梁的意外事故监控系统中,并且可以适应各种天气,具有全天候检测的能力。文章从系统特点、检测数据精度、系统的多功能性及系统操作的方便性等方面介绍了该系统的先进性、实用性。