Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Aggregated network level modeling and control of traffic in urban networks have recently gained a lot of interest due to unpredictability of travel behaviors and high complexity of physical modeling in microscopic level. Recent research has shown the existence of well-defined Macroscopic Fundamental Diagrams (MFDs) relating average flow and density in homogeneous networks. The concept of MFD allows to design real-time traffic control schemes specifically hierarchical perimeter control approaches to alleviate or postpone congestion. Considering the fact that congestion is spatially correlated in adjacent roads and it propagates spatiotemporaly with finite speed, describing the main pockets of congestion in a heterogeneous city with small number of clusters is conceivable. In this paper, we propose a three-step clustering algorithm to partition heterogeneous networks into connected homogeneous regions, which makes the application of perimeter control feasible. The advantages of the proposed method compared to the existing ones are the ability of finding directional congestion within a cluster, robustness with respect to parameters calibration, and its good performance for networks with low connectivity and missing data. Firstly, we start to find a connected homogeneous area around each road of the network in an iterative way (i.e. it forms a sequence of roads). Each sequence of roads, defined as ‘snake’, is built by starting from a single road and iteratively adding one adjacent road based on its similarity to join previously added roads in that sequence. Secondly, based on the obtained sequences from the first step, a similarity measure is defined between each pair of the roads in the network. The similarities are computed in a way that put more weight on neighboring roads and facilitate connectivity of the clusters. Finally, Symmetric Non-negative Matrix Factorization (SNMF) framework is utilized to assign roads to proper clusters with high intra-similarity and low inter-similarity. SNMF partitions the data by providing a lower rank approximation of the similarity matrix. The proposed clustering framework is applied in medium and large-size networks based on micro-simulation and empirical data from probe vehicles. In addition, the extension of the algorithm is proposed to deal with the networks with sparse measurements where information of some links is missing. The results show the effectiveness and robustness of the extended algorithm applied to simulated network under different penetration rates (percentage of links with data).     

Analysis of the effectiveness of control measures to mitigate road dust emissions in a regional network

The effectiveness of control measures to reduce road dust emissions is analyzed using a year’s data of road dust emissions collected with a mobile sampling platform and a survey of road maintenance practices in the Lake Tahoe Basin of Nevada and California US. Attributes such as sweeping practices, anti-icing, shoulder improvement, pavement condition, trackout, and abrasive material from road segments were analyzed with a feature subset selection algorithm. Street sweeping was found to be an effective means of controlling dust emissions from roads. Road dust from dirty tertiary roads served as a continuous source of suspendable material for adjacent high-speed roads in the winter time. To be most effective, emission control strategies require that not only primary roads, but all roads be swept after snow storms to recover applied abrasive material.     

Environmental traffic management — The end of the road?

This paper questions some aspects of the technical soundness and public acceptability of environmental traffic management scheme of the kind advocated in the Buchanan report, Traffic in Towns. Practical studies have shown that in inner city areas in particular, and perhaps older built-up areas in general, the concept cannot be adequately defended against a wealth of variety of criticism from those whom it most directly affects, i.e. the public. Participation exercises have revealed public fears that the road closures associated with schemes will ruin the viability of local shops, worsen the environmental conditions along local roads chosen as distributors, cause even greater congestion on the main road network due to displaced traffic, and impede the accessibility of local people to their homes. In the light of these, it is suggested a flexible policy be adopted incorporating changes to the forms of vehicles and the manner in which they are used as well as changes to the physical form of the road network.The opinions expressed in this paper do not necessarily reflect those of the organisations to which the authors are attached.     

Socially and environmentally appropriate urban futures for the motor car

In its relatively short life, the automobile has provided a level of mobility unlikely to have been feasible with a reliance on conventional forms of land based public transport. It has contributed in both a positive and negative way to the quality of life, transforming our cities, our way of life, and giving us a greater command over time and space. Concern over the undesirable social and environmental impacts has increased over time, with calls for governments to take action to reduce the automobile's dominant role. New investment in fixed-track public transport and bus priority systems together with strategies to discourage travel have been proposed to improve accessibility and to aid in cleaning up the physical environment. This paper reviews some of the issues facing society as it works to identify policies to achieve an economically and environmentally sustainabie future. There is a need for a broader set of policies to facilitate alternative land use-transport lifestyles while facing appropriate pricing signals. Some of the key issues are adjustments in the relative prices of location and transport, spatial incentives to make public transport economically viable (i.e. changing urban densities, zoning/incentive changes to allow more infill), road pricing (i.e. charging cars the economic cost of using the roads), new information technology systems (e.g. IVHS) to improve the efficiency and effectiveness of transport infrastructure, major improvements in the fuel efficiency of fossil fuelled vehicles, and alternative-fuelled vehicles (clean-air vehicles).     

Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots

Dial-a-ride problems are concerned with the design of efficient vehicle routes for transporting individual persons from specific origin to specific destination locations. In real-life this operational planning problem is often complicated by several factors. Users may have special requirements (e.g. to be transported in a wheelchair) while service providers operate a heterogeneous fleet of vehicles from multiple depots in their service area. In this paper, a general dial-a-ride problem in which these three real-life aspects may simultaneously be taken into account is introduced: the Multi-Depot Heterogeneous Dial-A-Ride Problem (MD-H-DARP). Both a three- and two-index formulation are discussed. A branch-and-cut algorithm for the standard dial-a-ride problem is adapted to exactly solve small problem instances of the MD-H-DARP. To be able to solve larger problem instances, a new deterministic annealing meta-heuristic is proposed. Extensive numerical experiments are presented on different sets of benchmark instances for the homogeneous and the heterogeneous single depot dial-a-ride problem. Instances for the MD-H-DARP are introduced as well. The branch-and-cut algorithm provides considerably better results than an existing algorithm which uses a less compact formulation. All seven previously unsolved benchmark instances for the heterogeneous dial-a-ride problem could be solved to optimality within a matter of seconds. While computation times of the exact algorithm increase drastically with problem size, the proposed meta-heuristic algorithm provides near-optimal solutions within limited computation time for all instances. Several best known solutions for unsolved instances are improved and the algorithm clearly outperforms current state-of-the-art heuristics for the homogeneous and heterogeneous dial-a-ride problem, both in terms of solution quality and computation time.     

道路环境对交通安全的影响及对策

交通安全,涉及到人、车以及道路和环境等很多因素。近年来,因为各种原因使得有关单位以及人员将更多的将精力放在人、车、道路这几个因素上,对道路交通环境没有过多的考虑和研究。深入研究道路交通环境,有利于道路交通的安全,文章通过对其的研究和分析,得出有关道路交通环境对交通安全的影响和解决措施,对于防止道路交通安全事故具有重要意义。同时为人们的生命财产的安全提供保障、对构建社会主义和谐社会具有很大帮助。     

Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments

In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%.     

The time-dependent pollution-routing problem

The Time-Dependent Pollution-Routing Problem (TDPRP) consists of routing a fleet of vehicles in order to serve a set of customers and determining the speeds on each leg of the routes. The cost function includes emissions and driver costs, taking into account traffic congestion which, at peak periods, significantly restricts vehicle speeds and increases emissions. We describe an integer linear programming formulation of the TDPRP and provide illustrative examples to motivate the problem and give insights about the tradeoffs it involves. We also provide an analytical characterization of the optimal solutions for a single-arc version of the problem, identifying conditions under which it is optimal to wait idly at certain locations in order to avoid congestion and to reduce the cost of emissions. Building on these analytical results we describe a novel departure time and speed optimization algorithm for the cases when the route is fixed. Finally, using benchmark instances, we present results on the computational performance of the proposed formulation and on the speed optimization procedure.     

Density/Flow reconstruction via heterogeneous sources and Optimal Sensor Placement in road networks

This paper addresses the two problems of flow and density reconstruction in Road Transportation Networks with heterogeneous information sources and cost effective sensor placement. Following a standard modeling approach, the network is partitioned in cells, whose vehicle densities change dynamically in time according to first order conservation laws. The first problem is to estimate flow and the density of vehicles using as sources of information standard fixed sensors, precise but expensive, and Floating Car Data, less precise due to low penetration rates, but already available on most of main roads. A data fusion algorithm is proposed to merge the two sources of information to estimate the network state. The second problem is to place sensors by trading off between cost and performance. A relaxation of the problem, based on the concept of Virtual Variances, is proposed and solved using convex optimization tools. The efficiency of the designed strategies is shown on a regular grid and in the real world scenario of Rocade Sud in Grenoble, France, a ring road 10.5 km long.     

A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside

On many urban low‐grade or branch roads, especially in medium or small cities in China, bicyclists and motorists commonly share the non‐barrier road surface. Because bicycles are unpredictable and unstable when moving, motorized vehicles must reduce their speed to safely approach and overtake them. In this study, the gradual deceleration process a motorized vehicle undergoes before it passes a bicycle was analyzed. The motorist was assumed to prefer a comfortable deceleration and to select a higher deceleration rate only when the distance to the bicycle was insufficient to reduce the car's speed to the expected value at a comfortable deceleration rate. Cellular automata (CA) simulations were used to reveal the flow characteristics of motorized vehicles reacting to bicycles traveling along the roadside, and the results show that for the general velocities of motorized vehicles and bicycles traveling on urban branch roads, the road capacity for motorized vehicles is not related to the number of bicycles present. However, the average travel time of motorized vehicles is significantly affected by the presence of bicycles when the number of motorized vehicles on the road is small. In addition, motorized vehicles' average travel time is more influenced by disturbances in the flow of motorized vehicles than by bicycles when the number of motorized vehicles on the road is large. Field observations and surveys were used to validate the traffic behaviors and simulation results. Copyright © 2016 John Wiley & Sons, Ltd.     

Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates

Data from connected probe vehicles can be critical in estimating road traffic conditions. Unfortunately, current available data is usually sparse due to the low reporting frequency and the low penetration rate of probe vehicles. To help fill the gaps in data, this paper presents an approach for estimating the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. A public data feed from transit buses in the city of San Francisco is used as an example data source. Low frequency updates (at every 200 m or 90 s) leaves much to be inferred. We first estimate travel time statistics along the road and queue patterns at intersections from historical probe data. The path is divided into short segments, and an Expectation Maximization (EM) algorithm is proposed for allocating travel time statistics to each segment. Then the trajectory with the maximum likelihood is generated based on segment travel time statistics. The results are compared with high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach, in estimating both the trajectory while moving and the stop positions and durations at intersections.     

Optimal road design through ecologically sensitive areas considering animal migration dynamics

With increasing land transportation requirements in both urban and rural areas, roads are encroaching ever more on animal habitats, where collisions with vehicles are a leading contributor to wildlife mortality. While road designers recognise the importance of accounting for such impacts at the design level, existing approaches simply either ignore viable habitat or avoid such regions entirely. Respectively, this can result in road alignments that are overly damaging to vulnerable species or prohibitively expensive to build and operate. The research presented in this paper investigates the effects of explicitly accounting for animal mortality on the design of a road through an ecologically sensitive area. The model presented achieves this by incorporating a spatially-explicit animal migration and road mortality model into an accepted optimal road alignment algorithm to propose low-cost roads that maintain the animal population above a minimum threshold by the end of a specified design horizon. The new method was applied to an example scenario to demonstrate the effect of setting a minimum required animal population on the road design. This model was able to consistently produce a road that met a minimum required species conservation benefit. This reflected a major improvement over the model that ignored animal habitats while only requiring a minor increase in construction and operating costs compared to the model that avoids habitat.     

Modeling Capacity Reliability of Minor Roads at At-Grade Un-Signalized Intersections for Potential Performance Evaluation

Abstract

Given stochastic features of the demands on both the major road and the minor road at an at-grade un-signalized intersection, the capacity of the minor road is viewed as the vulnerable and critical part impacting on the overall capacity of the intersection. To facilitate the analysis of intersection performance reliability, the capacity reliability of the minor road is defined as the probability that the capacity of the minor road can accommodate a certain traffic demand at a certain degree of saturation. The headway distribution of traffic stream on major road is reflected by three types of distributions, namely, exponential distribution, shifted exponential distribution, and Cowan's M3 distribution. Based on field observations, the volumes on both major roads and minor roads are treated as correlated normal random variables. This paper presents the methods for modeling capacity reliability of the minor road at an at-grade un-signalized priority intersection. A method based on first-order reliability method is used to model the capacity reliability index. As important associated factors of capacity reliability analysis, the methods for modeling and analyzing capacity sensitivity of the minor road and reserve capacity of the priority intersection are also presented. A procedure for evaluating the intersection potential performance using capacity reliability, sensitivity and reserve capacity is developed and demonstrated with a numerical example. Finally, some new findings from the case studies are summarized.     

A Granular Variable Tabu Neighborhood Search for the capacitated location-routing problem

This paper proposes a new heuristic algorithm for the Capacitated Location-Routing Problem (CLRP), called Granular Variable Tabu Neighborhood Search (GVTNS). This heuristic includes a Granular Tabu Search within a Variable Neighborhood Search algorithm. The proposed algorithm is experimentally compared on the benchmark instances from the literature with several of the most effective heuristics proposed for the solution of the CLRP, by taking into account the CPU time and the quality of the solutions obtained. The computational results show that GVTNS is able to obtain good average solutions in short CPU times, and to improve five best known solutions from the literature. The main contribution of this paper is to show a successful new heuristic for the CLRP, combining two known heuristic approaches to improve the global performance of the proposed algorithm for what concerns both the quality of the solutions and the computing times required to find them.     

Factors affecting mortality of vertebrates on the roads in the Czech Republic

This paper summarizes the results of monitoring animals killed by vehicles on selected roads in the Czech Republic as well as the circumstances of these accidents. Relationships between species of road-killed animals and factors associated with these accidents were evaluated, such as type of road and traffic density, distance from the built-up area, state of vegetation around the road, terrain topology with regard to the road, and the impact of the month/season.     

Generating complete all-day activity plans with genetic algorithms

Activity-based demand generation contructs complete all-day activity plans for each member of a population, and derives transportation demand from the fact that consecutive activities at different locations need to be connected by travel. Besides many other advantages, activity-based demand generation also fits well into the paradigm of multi-agent simulation, where each traveler is kept as an individual throughout the whole modeling process. In this paper, we present a new approach to the problem, which uses genetic algorithms (GA). Our GA keeps, for each member of the population, several instances of possible all-day activity plans in memory. Those plans are modified by mutation and crossover, while bad instances are eventually discarded. Any GA needs a fitness function to evaluate the performance of each instance. For all-day activity plans, it makes sense to use a utility function to obtain such a fitness. In consequence, a significant part of the paper is spent discussing such a utility function. In addition, the paper shows the performance of the algorithm to a few selected problems, including very busy and rather non-busy days.     

基于关联规则的重大道路交通事故影响因素分析

相比于一般交通事故,重大道路交通事故的特征及其影响机理会有所差异,本文旨在研究重大道路交通事故的分布特征及其主要影响因素。收集2014至2018年的重大道路交通事故数据,从驾驶员行为、车辆状况、道路线形和时空分布方面对重大道路交通事故的基本特征进行分析,采用关联规则技术深入挖掘重大道路交通事故多因素的影响机理。从人、车、道路和环境四个方面,重点讨论了重大道路交通事故中的两因素和三因素交互作用的影响机理,并据此提出了针对性的事故预防措施。     

Agricultural vehicles and sustainable safe road traffic: solving conflicts on arterial highways

Addressing the issues of traffic safety in rural areas presents a constant challenge. The mix of light and heavy vehicles and the considerable differences in speed among these traffic participants result in high risks and delays for the faster vehicles. Agricultural vehicles (AVs) in particular have such an impact on traffic, especially when using arterial highways. This paper reviews the problems of safety and delays that AVs cause on arterial highways, and the appropriate mitigation. The concept of 'sustainable safety' in The Netherlands focuses on these problems, because of the proposed construction of parallel roads alongside all arterial highways. However, Dutch accident statistics cannot justify the high costs for the construction of parallel roads alongside 7000 km of arterial highways. Delays experienced by fast traffic are another reason for separating AVs from other road users with parallel roads. Alternative measures alongside the arterial highway, such as passing bays, restricting AVs to travelling at off-peak only and improving the conspicuity of the AVs, may be more cost-effective ways of reducing delays and/or improving traffic safety on arterial highways. Another solution may be to eliminate the need for AVs to use the arterial highway by altering their routes. For this purpose, land reallocation projects (as practised in Holland) can provide a useful tool.     

Cooperative GNSS positioning aided by road-features measurements

Cooperation between road users through V2X communication is a way to improve GNSS localization accuracy. When vehicles localization systems involve standalone GNSS receivers, the resulting accuracy can be affected by satellite-specific errors of several meters. This paper studies how road-features like lane marking detected by on-board cameras can be exploited to reduce absolute position errors of cooperative vehicles sharing information in real-time in a network. The algorithms considered in this work are based on a error bounded set membership strategy. In every vehicle, a set membership algorithm computes the absolute position and an estimation of the satellite-specific errors by using raw GNSS pseudoranges, lane boundary measurements and a 2D georeferenced road map which provides absolute geometric constraints. As lane-boundary measurements provide essentially cross-track corrections in the position estimation process, cooperation enables the vehicles to improve their own estimates thanks to the different orientation of the roads. Set-membership methods are very efficient to solve this problem since they do not involve any independence hypothesis of the errors and so, the same information can be used several times in the computation. Such class of algorithm provides a novel approach to improve position accuracy for connected vehicles guaranteeing the integrity of the computed solution which is pivoting for automated automotive systems requiring guaranteed safety-critical solutions. Results from simulations and real experiments show that sharing position corrections reduces significantly satellite-specific GNSS errors effects in both cross-track and along-track components. Moreover, it is shown that lane-boundary measurements help reducing estimation errors for all the networked vehicles even those which are not equipped with an embedded perception system.