Influences of the driver’s bounded rationality on micro driving behavior,fuel consumption and emissions

In this paper, we propose an extended car-following model to study the influences of the driver’s bounded rationality on his/her micro driving behavior, and the fuel consumption, CO, HC and NO_X of each vehicle under two typical cases, where Case I is the starting process and Case II is the evolution process of a small perturbation. The numerical results indicate that considering the driver’s bounded rationality will reduce his/her speed during the starting process and improve the stability of the traffic flow during the evolution of the small perturbation, and reduce the total fuel consumption, CO, HC and NO_X of each vehicle under the above two cases.     

Study on road section environmental traffic capacity model and algorithm under double constraints

The coordinated development of city traffic and environment is a key research content in traffic field in twenty-first Century. Among them, road section environmental traffic capacity analysis is one of the important research issues. It can provide solid theoretical basis and reliable data support for road network traffic optimization control, road traffic pollution control and city traffic structure optimization. This paper analyzed main factors which impacted environmental traffic capacity from two aspects, including road capacity constraint conditions and road traffic pollution control constraint conditions. Then, road section environmental traffic capacity optimization model was established, and method of improved augmented Lagrange function was used to solve the model. Case study showed that, (1) The environmental traffic capacity optimal model and methodology were effective; (2) In order to ensure road section environmental traffic capacity greater than (or equal to) road capacity, some measures could be taken including adjusting motor vehicle type proportion as well as improving emission characteristics of motor vehicles exhausting pollutants.     

Optimality versus run time for isolated signalized intersections

Simulation-based optimization of traffic signal timing has become pervasive and popular, in the field of traffic engineering. When the underlying simulation model is well-trusted and/or well-calibrated, it is only natural that typical engineers would want their signal timing optimized using the judgment of that same model. As such, it becomes important that the heuristic search methods typically used by these optimizations are capable of locating global optimum solutions, for a wide range of signal systems. However off-line and real-time solutions alike offer just a subset of the available search methods. The result is that many optimizations are likely converging prematurely on mediocre solutions. In response, this paper compares several search methods from the literature, in terms of both optimality (i.e., solution quality) and computer run times. Simulated annealing and genetic algorithm methods were equally effective in achieving near-global optimum solutions. Two selection methods (roulette wheel and tournament), commonly used within genetic algorithms, exhibited similar effectiveness. Tabu searching did not provide significant benefits. Trajectories of optimality versus run time (OVERT) were similar for each method, except some methods aborted early along the same trajectory. Hill-climbing searches always aborted early, even with a large number of step-sizes. Other methods only aborted early when applied with ineffective parameter settings (e.g. mutation rate, annealing schedule). These findings imply (1) today’s products encourage a sub-optimal “one size fits all” approach, (2) heuristic search methods and parameters should be carefully selected based on the system being optimized, (3) weaker searches abort early along the OVERT curve, and (4) improper choice of methods and/or parameters can reduce optimization benefits by 22–33%.     

Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach

An important question for the practical applicability of the highly efficient traffic intersection control is about the minimal level of intelligence the vehicles need to have so as to move beyond the traffic light control. We propose an efficient intersection traffic control scheme without the traffic lights, that only requires a majority of vehicles on the road to be equipped with a simple driver assistance system. The algorithm of our scheme is completely decentralised, and takes into full account the non-linear interaction between the vehicles at high density. For vehicles approaching the intersection in different directions, our algorithm imposes simple interactions between vehicles around the intersection, by defining specific conditions on the real-time basis, for which the involved vehicles are required to briefly adjust their dynamics. This leads to a self-organised traffic flow that is safe, robust, and efficient. We also take into account of the driver comfort level and study its effect on the control efficiency. The scheme has low technological barrier, minimal impact on the conventional driving behaviour, and can coexist with the traffic light control. It also has the advantages of being easily scalable, and fully compatible with both the conventional road systems as well as the futuristic scenario in which driverless vehicles dominate the road. The mathematical formulation of our scheme permits large scale realistic numerical simulations of busy intersections, allowing a more complete evaluation of the control performance, instead of just the collision avoidance at the intersection.     

Individual exposure to traffic related air pollution across land-use clusters

In this study, we estimated the transportation-related emissions of nitrogen oxides (NO_x) at an individual level for a sample of the Montreal population. Using linear regression, we quantified the associations between NO_x emissions and selected individual attributes. We then investigated the relationship between individual emissions of NO_x and exposure to nitrogen dioxide (NO₂) concentrations derived from a land-use regression model. Factor analysis and clustering of land-uses were used to test the relationships between emissions and exposures in different Montreal areas. We observed that the emissions generated per individual are positively associated with vehicle ownership, gender, and employment status. We also noted that individuals who live in the suburbs or in peripheral areas generate higher emissions of NO_x but are exposed to lower NO₂ concentrations at home and throughout their daily activities. Finally, we observed that for most individuals, NO₂ exposures based on daily activity locations were often slightly more elevated than NO₂ concentrations at the home location. We estimated that between 20% and 45% of individuals experience a daily exposure that is largely different from the concentration at their home location. Our findings are relevant to the evaluation of equity in the generation of transport emissions and exposure to traffic-related air pollution. We also shed light on the effect of accounting for daily activities when estimating air pollution exposure.     

Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique

The objective of this research was to develop a model for validating traffic accident locations that would be applicable worldwide, regardless of linguistic or cultural differences. In order to achieve this, a Volunteered Geographic Information (VGI) dataset was used, the OpenStreetMap (OSM) project. To test the developed model, a total of 8550 accidents with fatal or non-fatal injuries that occurred in the City of Zagreb from 2010 to 2014 were evaluated. Traffic accident data was collected using the pen-and-paper method while the traffic accident locations were determined using Global Positioning System (GPS) receivers embedded within police vehicles. This form of data entry invariably introduces errors in both geometric and contextual attributes. To fully counteract these errors, the developed model consists of two key concepts: the Jaro–Winkler string matching technique and the Inverse Distance Weighting method. Over 66% of traffic accident locations were validated, which is an increase of 15% when compared to the classical approach. The model outlined in this paper shows a significant improvement in estimating the correct location of traffic accidents. This in turn results in a drastic decrease in resources needed to estimate the quality of accident locations.     

Traffic guidance–perimeter control coupled method for the congestion in a macro network

Macroscopic fundamental diagram (MFD) describes the macro relationship between a network vehicle density and a network space mean flow, without requiring the mastery of complex origin to destination data. Thus, MFD provides an opportunity for the macro control of urban road network. However, most of the existing MFD control methods ignore the active role of traffic guidance in solving congestion problems. This study presents a traffic guidance–perimeter control coupled (TGPCC) method to improve the performance of macroscopic traffic networks. The method considers the optimal cumulative volume of a network as the goal and establishes a programming function according to the network equilibrium rule of traffic flow amongst multiple MFD sub-regions, which regards the minimum delay of network, as the objective. The Logit model for the compliance rate of driver route guidance is established by the stated preference survey. Moreover, the perimeter control (PC) method is proposed for adjusting the phase split of intersections. Finally, three schemes, namely, the TGPCC, PC and the method without PC and guidance are tested on a network with four well-defined MFD sub-regions. Results show that the TGPCC addresses the issue of congestion and decreases the total delay accordingly.     

A data fusion approach for real-time traffic state estimation in urban signalized links

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.     

单片机在可遥控的十字路口机动车红绿灯控制系统的使用

本文探讨了单片机在可遥控的十字路口机动车红绿灯控制系统的使用,其中包括系统设计思想、工作原理、软件程序设计。     

突出重点服务大局实现交通规费征稽工作新突破

文章结合目前广西交通规费征稽工作现状,分析了2006年征稽工作的新形势,提出今年规费征收工作应重点在“保费源、查漏征、治外挂”三个方面下功夫,堵住各种偷、逃、漏规费的渠道,力争2006年广西交通规费征收实现新突破。