Urban Traffic Conditions Research Articles

Urban Network Traffic State Evaluation Based on a Traffic State Coefficient

AbstractUrban traffic network state is evaluated based on the traffic state evaluation of its component units (junctions and road sections). However, previous research viewed the intersection into road sections more from the meso perspective, ignoring the effects of the intersection on traffic network state. In the current work, the basic parameters of traffic network state evaluation such as the intersection entrance saturation and the mean space speed of road sections are determined by combining the macro road network state evaluation and microscopic traffic state analysis. The traffic network state coefficient is defined as the traffic flow moving resistance, and the critical value of the traffic network coefficient is studied and determined. A new method of road traffic network state analysis is proposed, and the whole simulated road network state is analyzed. Finally, the whole road network state is evaluated and analyzed according to accessibility and connectivity.

Reliability analysis of pedestrian safety crossing in urban traffic environment

This paper describes the pedestrian safety crossing behaviour at signalized crosswalks in an urban traffic environment based on human reliability analysis. In our research, pedestrians’ waiting durations are modelled by a non-parametric and two parametric reliability models that recognize the effects of covariates. The covariates include pedestrian personal characteristics and urban traffic conditions in order to reflect the effects of human factors and internal environment comprehensively. The results indicate that most pedestrians show distinct time-dependent reliability but a few pedestrians are too impatient to wait for the lights changes.

Efficient Routing of Emergency Vehicles under Uncertain Urban Traffic Conditions

Emergency-vehicle drivers who aim to reach their destinations through the fastest possible routes cannot rely solely on expected average travel times. Instead, the drivers should combine this travel-time information with the characteristics of data variation and then select the best or optimal route. The problem can be formulated on a graph in which the origin point and destination point are given. To each arc in the graph a random variable is assigned, characterized by the expected time to traverse the arc and the variance of that time. The problem is then to minimize the total origin-destination expected time, subject to the constraint that the variance of the travel time does not exceed a given threshold. This paper proposes an exact pseudo-polynomial algorithm and an e-approximation algorithm (so-called FPTAS) for this problem. The model and algorithms were tested using real-life data of travel times under uncertain urban traffic conditions and demonstrated favorable computational results.

Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.

Urban Traffic State Estimation Considering Resident Travel Characteristics and Road Network Capacity

Urban traffic state is usually measured by vehicle average travel speed. It reflects the resident travel behavior on urban road network, and changes with the actual matching status of traffic demand and supply. In this paper, the traffic supply is estimated by the ideal carrying capacity, and the traffic demand is evaluated by vehicle traffic intensity considering resident travel characteristics. In this way, the network carrying capacity and traffic intensity are compared by equative unit. By introducing the intensity/capacity ratio, the matching degree between traffic demand and supply is determined, and the traffic state is identified based on average vehicle speed. In the last section, the 2005 field data of Beijing city is used to test and verify the proposed method.

Development and Application of CHANSIGNAL Signal Timing Software System

Considering the mixed traffic flow of current urban traffic condition in our country, the paper develops the CHANSIGNAL signal timing system based on the improved TRRL signal timing model. Through simulation analysis and case verification, the system presents the advantages of high accuracy, calculating speed in signal timing for intersection. The system significantly improves the traffic operating condition, which proves the system’s validity and practicality.

Evaluation Analysis and Optimization Design of Urban Road Traffic Condition Based on VISSIM

Based on the survey of the traffic flow characteristics in the intersection, combined with the traffic flow basic principle and the existing research results at home and abroad, the road congestion condition is evaluated by determining the microcosmic evaluation index coefficient by VISSIM microcosmic simulation software. Then, the reasonable measures are put forward to improve urban road congestion condition. The analysis shows that the residents’ trip efficiency is improved and the quality of the urban road traffic condition is ameliorated.

Urban Transportation Crowded Recognition Technology and Application

The urban road traffic congestion has not only brought many inconvenient for people's routine work and life, but also will restrict the growth of the economical, to accelerate the urban environment worsening and serious influence the city sustainable development. This paper studies based on the dynamic detection of urban road traffic congestion condition recognition technology can fast and accurate discover in the road network which already had the traffic congestion or soon occurs, then estimated the crowded proliferation scope and duration, which are advantageous to carry on the transportation induction and the traffic control promptly. And according to the different target client to the different emphasis point to the distinguish algorithm, has designed the urban road traffic congestion recognition grading warning system.

Selecting a Location for a City Logistics Facility: A Fuzzy Synthetic Evaluation Method

The strategic location of city logistics facilities may help to establish more efficient urban logistics systems, reduce social and environmental costs of urban freight transport, and improve urban traffic conditions. In addition, it may allow a number of shippers or freight carriers to jointly operate freight vehicles and terminals or information systems while allowing them to have the capability to provide higher levels of services to their customers. This paper considers the problem of selecting a location for a city logistics facility while considering linguistic factors. This paper identifies the important factors in the selection of a location for a city logistics facility by performing a case study which applies the analytic hierarchy process method on data from Chongqing, China. The optimal location in Chongqing is then determined by using the fuzzy synthetic evaluation method. The results of this paper are expected to help municipal governments select appropriate locations for city logistics facilities and quantify the advantages and disadvantages of alternative locations.

Active intelligent parking guidance system

Based on the intrinsic features of spatial distribution,temporal distribution and high dynamic of parking activities,a negotiation approach was introduced to the design of an intelligent parking guidance system.The IEEE FIPA compliant multi-Agent system called active negotiation-based intelligent parking guidance system(AIPGIS) was proposed.The architecture,operation mechanism,negotiation algorithms and characteristics were analyzed and presented.The AIPGIS can implement effective sharing of urban traffic state information and strengthen the coordination and decision-making capacities of the active Agents.

Selecting a Location for a City Logistics Facility: A Fuzzy Synthetic Evaluation Method

The strategic location of city logistics facilities may help to establish more efficient urban logistics systems, reduce social and environmental costs of urban freight transport, and improve urban traffic conditions. In addition, it may allow a number of shippers or freight carriers to jointly operate freight vehicles and terminals or information systems while allowing them to have the capability to provide higher levels of services to their customers. This paper considers the problem of selecting a location for a city logistics facility while considering linguistic factors. This paper identifies the important factors in the selection of a location for a city logistics facility by performing a case study which applies the analytic hierarchy process method on data from Chongqing, China. The optimal location in Chongqing is then determined by using the fuzzy synthetic evaluation method. The results of this paper are expected to help municipal governments select appropriate locations for city logistics facilities and quantify the advantages and disadvantages of alternative locations.

Development and Evaluation of On-Board Measurement System for Nanoparticle Emissions from Diesel Engine

This work has focused on the development and evaluation of an experimental set-up to measure in real time and with on-board equipments the particle emissions of diesel vehicle minimizing the uncertainties associated to dilution ratio, the length of the transfer line and the sampling point in the engine exhaust pipe. Its suitability has been verified by ensuring the repeatability of the results in dynamometer tests reproducing standard circuits, as well as in closed circuits and in real urban traffic in Madrid, Spain. The experience derived from this work has been very useful, contributing to the advance in the measurement of particle number and size distribution in real time using on-board equipment and bringing us closer to understanding the relationship between the physical characteristics of the particles emitted by a diesel engine and its operation in real urban traffic conditions. The proposed on-board system has provided very satisfactory results.

Evaluation of Nonintrusive Technologies for Traffic Detection, Phase 3

The third phase of the project on evaluation of nonintrusive technologies for traffic detection is a pooled-fund study in which field tests of selected nonintrusive sensors were conducted to determine their accuracy for volume, speed, and classification by length and by axle configuration. Sensors were evaluated in a variety of traffic and environmental conditions at two freeway test sites, with additional tests performed at both signalized and unsignalized intersections. Emphasis was placed on urban traffic conditions, such as heavy congestion, and varying weather and lighting conditions. Although previous tests evaluated sensors’ volume and speed accuracy, the current generation of nonintrusive sensors introduces robust classification capabilities. New technologies, such as ground-mounted laser sensors and improved radar, contribute to this improved performance. Overall, the sensors performed better than their counterparts in previous phases of research for volume and speed accuracy. However, the additional classification capabilities had mixed results. The length-based sensors were generally able to report vehicle lengths within their tolerances, and the axle-based sensors provided accurate interaxle measurements, but significant errors were found in relating these data with a standardized classification scheme, such as FHWA's 13-class scheme. Agencies must perform independent analysis of their classification schemes to determine whether nonintrusive sensors will provide acceptable results.

Urban Infrastructure-to-Vehicle Traffic Data Dissemination Using UEP Rateless Codes

In this paper we propose an end-to-end solution for urban infrastructure-to-vehicle traffic data delivery based on a class of unequal error protection (UEP) rateless codes called expanding window fountain (EWF) codes. The proposed solution relies on attractive features that rateless codes introduce to networks with unpredictable dynamics: the universal capacity approaching property which is well-matched to time-varying behavior of wireless links, and the innovative nature of each encoded packet which makes both time-consuming retransmission and content-reconciliation mechanisms unnecessary. Furthermore, usage of EWF codes allows separation of delivered data in importance classes with different error protection and recovery time guarantees, enabling mobile users to retrieve more important information more reliably and in shorter time span, thus making the proposed solution suitable for time-critical services. The addressed urban communication scenario consists of large number of sensors that sample and relay traffic flow information to network of Access Points (APs). APs use the existing underlying communication infrastructure, such as metropolitan area networks (MANs), to exchange traffic flow data, encode it using EWF coding principles, and finally disseminate it to roaming vehicles that join the network service in an ad-hoc manner in order to retrieve information regarding the surrounding environment. The proposed approach is suitable for real-time applications, such as frequent periodic reporting of urban traffic conditions, that could be used by on-board computers to provide improved navigation for end-users.

Location Decision of City Logistics Facility Based on Fuzzy Synthetic Evaluation

The concept of city logistics facilities is to help establish more efficient urban logistics systems for both private companies and society, reduce the total social and environmental costs of urban freight transport and improve the urban traffic conditions on the road network. This study adopts a fuzzy synthetic evaluation method to determine the optimal location of city logistics facilities.

Urban expressway traffic state forecasting based on multimode maximum entropy model

The accurate and timely traffic state prediction has become increasingly important for the traffic participants, especially for the traffic managements. In this paper, the traffic state is described by Micro-LOS, and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework, the different features like temporal and spatial features of traffic systems, regional traffic state are integrated simultaneously, and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.

Assessment of Real World On-Road Vehicle Emissions under Dynamic Urban Traffic Conditions in Delhi

Urban transport plays an important role in city development and is one of the major sources of environmental pollution. With a view to understand the relationship between the vehicle emissions and traffic control measures, this paper intends to evaluate the relationship between traffic flow and vehicle emissions using the advanced portable emission measurement system, OEM-2100. Real world on-road data collection of emissions for NOx HC, CO and C02 along with engine parameters such as speed was collected and evaluated during acceleration, deceleration, cruising, and idling under dynamic urban traffic conditions in Delhi. The relationship between vehicle emissions and a commonly used traffic measure, control delay, was investigated. Results showed that emissions were episodic in nature and high emissions rates occurred during acceleration, while vehicle emissions were low when the vehicle was idling. Further emission rates of NOx, HC, CO, and CO2 during delay events were significantly higher than during non-delay events.

Real-time urban traffic monitoring with global positioning system-equipped vehicles

Real-time traffic conditions are useful information based on which many adaptive traffic solutions work. In this study, the authors present a new approach for real-timely monitoring urban traffic with global positioning system (GPS)-equipped vehicles, which provides estimation of urban traffic conditions in real time. The approach first real-timely collects GPS trace data from GPS-equipped vehicles on the urban road network. Then, it periodically clusters the collected data of several minutes, calculates estimated space mean speed (eSMS) and translates eSMS to smooth indexes (denoting traffic conditions). Compared with existing work, the presented one: (i) applies an effective map matching method to cluster GPS trace data; (ii) excludes traffic signal's misleading influences on traffic condition estimation and (iii) judges traffic conditions based on an estimated critical traffic flow characteristic. Some experiments based on GPS taxi scheduling data of Shanghai, China are provided to demonstrate performance of this work.

A Real-Time Urban Traffic Detection Algorithm Based on Spatio-Temporal OD Matrix in Vehicular Sensor Network

Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on originaldestination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.