Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work.

Short-term traffic forecasting: Where we are and where we’re going

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them Issues such as processing time, computational power, and recognition rate are also addressed, when available Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment

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License Plate Recognition From Still Images and Video Sequences: A Survey

How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The traditional input-output approach for queue length estimation can only handle queues that are shorter than the distance between vehicle detector and intersection stop line, because cumulative vehicle count for arrival traffic is not available once the detector is occupied by the queue. In this paper, instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using high-resolution "event-based" traffic signal data, and applying Lighthill-Whitham-Richards (LWR) shockwave theory, we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues. Variations of the queue length estimation model are also presented when "event-based" data is not available. Our models are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy. Limitations of the proposed model are also discussed in the paper.

Real-time queue length estimation for congested signalized intersections

Traffic congestion occurs frequently at downtown intersections during rush hours, at road construction zones as well as at accident sites. Under such circumstances, traffic flow exceeds intersection capacity causing queuing of automobiles that cannot be eliminated in one signal cycle. In this paper, we present a timing decision methodology which considers the whole oversaturation period. Discrete dynamic optimization models are developed and an algorithm to solve them is presented. The optimal cycle length and the optimal assigned green time for each approach are determined for the case of two-phase control. The application of the performance index model to certain multi-phase signals in common use is also introduced. Evaluation results indicate that the proposed discrete type performance index model is a more appropriate design for congested traffic signal timing control.

Optimal signal timing for an oversaturated intersection

This paper proposes a dynamic method to control an oversaturated traffic signal network by utilizing a bang-bang like model for the oversaturated intersections and TRANSYT-7F for the undersaturated intersections. An algorithm entitled the maximal progression possibility determines the optimal operating procedure by searching for progression routes and smoothing the transition between successive control cycles. The proposed model integrates two different operating procedures: saturated and undersaturated. The method was tested in a network comprising a core area of 12 initially oversaturated intersections that are surrounded by thirteen undersaturated intersections. It was concluded that the proposed method is much more effective in relieving congestion in a network than TRANSYT-7F. Since TRANSYT-7F is not suited for oversaturated network signal timing control, it can be mitigated through integration with the proposed model.

Modeling and optimization of an oversaturated signalized network

This paper develops an onboard decision module for issuing appropriate warnings when the equipped vehicle's traveling trajectory is irregular. Potential vehicle accidents exist if the driver is distracted by fatigue, drowsiness, food, phone use, and talking or is under the influence of alcohol or drugs, etc. Usual freeway accidents include lateral and rear-end collisions. Detecting vehicle behavior is a good way to measure the security of driving. This paper presents two modules. An unexpected lane departure avoidance module is utilized to prevent lateral collision. This module issues warnings when the vehicle approaches an irregular departure from the middle of the lane. Radial basis probability networks are applied to distinguish between normal lane change and lane departure. A rear-end collision avoidance module is used to issue warnings to avoid longitudinal accidents. This module reflects driver perceptions of environmental influence with a warning value and the warning threshold by neural networks and fuzzy membership functions, respectively. The proposed modules are satisfactory according to simulations and field tests.

Onboard Measurement and Warning Module for Irregular Vehicle Behavior

This work examines the positive effects on highway capacity implementing automatic vehicle control. Mixed traffic streams of a mimic freeway interchange simulate an on-ramp section with autopilot-equipped vehicles and normal manual vehicles. The simulation illustrates characteristics of speed, volume and concentration of mixed flows. The capacity trend is presented with mixed ratios of equipped cars and its market occupation rate. In order to make current highways more efficient during the transition stage, general rules for traffic control are proposed.

Analysis of characteristics of mixed traffic flow of autopilot vehicles and manual vehicles

Frequently implemented at freeway accesses to streamline traffic, ramp-metering control strategy is often implemented during rush hours in heavily congested areas. This paper presents a novel ramp-metering control model capable of optimizing mainline traffic by providing metering rates for accesses within the control segments. Based on Payne's continuum traffic stream model, a linear dynamic model with a quadratic objective function is constructed for integrated-responsive ramp-metering control. Incorporating on-line origin–destination (OD) estimation of co-ordinated interchanges into the proposed model increases efficiency of the control. In addition, an iterative algorithm is proposed to obtain the optimal solution. Simulation results demonstrate the robustness of the proposed model and its ability to streamline freeway traffic while avoiding traffic congestion.

Tang-Hsien Chang

Papers

Optimal signal timing for an oversaturated intersection

Modeling and optimization of an oversaturated signalized network

Onboard Measurement and Warning Module for Irregular Vehicle Behavior

Analysis of characteristics of mixed traffic flow of autopilot vehicles and manual vehicles

Optimization of mainline traffic via an adaptive co-ordinated ramp-metering control model with dynamic OD estimation