TRANSYT: traffic network study tool

In this paper, we construct a new car-following model with inter-vehicle communication (IVC) to study the driving behavior under an accident. The numerical results show that the proposed model can qualitatively describe the effects of IVC on each vehicle’s speed, acceleration, movement trail, and headway under an accident and that the new model can overcome the full velocity difference (FVD) model’s shortcoming that collisions occur under an accident, which illustrates that the new model can better describe the driving behavior under an accident than the FVD model.

A new car-following model with consideration of inter-vehicle communication

This article proposes to solve the oversatu- rated network traffic signal coordination problem us- ing the Ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the consid- ered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipation of queues and removal of block- ages as opposed tothe solecost minimization usually per- formed for undersaturation conditions. Two scenarios are considered and results are rigorously compared with solutions obtained using the genetic algorithm (GA), tra- ditionally employed to solve oversaturated conditions. ACO is shown to be consistently more effective for a larger number of trials and to provide more reliable so- lutions. Further, as a master-slave parallelism is possible for the nature of ACO algorithm, its implementation is suggested to reduce the overall execution time allowing the opportunity to solve real-time signal control systems. only a short time, the time to clear the network may be significant. Costs of infrastructure renewal and expan- sion may be cost prohibitive, and, under limited bud- gets, strategies are needed that enhance the mobility

Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

Macro-level pedestrian and bicycle crash analysis: incorporating spatial spillover effects in dual state count models

BACKGROUND: It is estimated that by 2020, road traffic crashes will have moved from ninth to third in the world ranking of burden of disease, as measured in disability adjusted life years. The prevention of road traffic injuries is of global public health importance. Measures aimed at reducing traffic speed are considered essential to preventing road injuries; the use of speed cameras is one such measure. OBJECTIVES: To assess whether the use of speed cameras reduces the incidence of speeding, road traffic crashes, injuries and deaths. SEARCH STRATEGY: We searched the following electronic databases covering all available years up to March 2010; the Cochrane Library, MEDLINE (WebSPIRS), EMBASE (WebSPIRS), TRANSPORT, IRRD (International Road Research Documentation), TRANSDOC (European Conference of Ministers of Transport databases), Web of Science (Science and Social Science Citation Index), PsycINFO, CINAHL, EconLit, WHO database, Sociological Abstracts, Dissertation Abstracts, Index to Theses. SELECTION CRITERIA: Randomised controlled trials, interrupted time series and controlled before-after studies that assessed the impact of speed cameras on speeding, road crashes, crashes causing injury and fatalities were eligible for inclusion. DATA COLLECTION AND ANALYSIS: We independently screened studies for inclusion, extracted data, assessed methodological quality, reported study authors' outcomes and where possible, calculated standardised results based on the information available in each study. Due to considerable heterogeneity between and within included studies, a meta-analysis was not appropriate. MAIN RESULTS: Thirty five studies met the inclusion criteria. Compared with controls, the relative reduction in average speed ranged from 1% to 15% and the reduction in proportion of vehicles speeding ranged from 14% to 65%. In the vicinity of camera sites, the pre/post reductions ranged from 8% to 49% for all crashes and 11% to 44% for fatal and serious injury crashes. Compared with controls, the relative improvement in pre/post injury crash proportions ranged from 8% to 50%. AUTHORS' CONCLUSIONS: Despite the methodological limitations and the variability in degree of signal to noise effect, the consistency of reported reductions in speed and crash outcomes across all studies show that speed cameras are a worthwhile intervention for reducing the number of road traffic injuries and deaths. However, whilst the the evidence base clearly demonstrates a positive direction in the effect, an overall magnitude of this effect is currently not deducible due to heterogeneity and lack of methodological rigour. More studies of a scientifically rigorous and homogenous nature are necessary, to provide the answer to the magnitude of effect. Language: en

/pdf/speed-cameras-for-the-prevention-of-road-traffic-injuries-ch12laosbe.pdf

Speed cameras for the prevention of road traffic injuries and deaths

Second order macroscopic traffic flow models are often used to replicate non-linear traffic flow phenomena such as phantom traffic jams or traffic instabilities. In contrast to the (first order) Lighthill–Whitham–Richards (LWR) traffic model, which assumes an equilibrium speed-density relationship (or so-called fundamental diagram), the second order model uses one more dynamic equation to describe the evolution of the speed, therefore allows the speed to fluctuate around the equilibrium diagram. In general, in the second order model, a given model parameter set may exhibit traffic instabilities due to a small initial traffic perturbation (e.g. lane-changing or sudden deceleration). Therefore, small changes of parameter set in second order models will lead to completely different model performance, which consequently leads to a complex calibration effort and hence prohibits its real-life application as compared to the LWR model. So far relatively few calibration results for general macroscopic traffic flow models have been reported. To contribute to the state-of-the-art, this paper puts forward an effort to find global optimal parameters of a second order macroscopic traffic model using a stochastic optimization approach, namely cross entropy method (CEM). Basically, the CEM is set up to solve combinatorial optimization problems so the main novelty of this paper is to apply the CEM to solve continuous multi-extremal optimization problems in transportation through the use of the Kernel density estimation method. Numerical studies are carried out to show that the Kernel-based CEM can search for the global optimal model parameters in a second order model and is a promising method for the calibration of traffic models in general.

Calibration of second order traffic models using continuous cross entropy method

Child pedestrian casualties and deprivation

The effect of speed enforcement cameras on accidents continues to be a source of considerable controversy. Recent headlines claim that they save around 100 lives a year in the UK while opponents still argue that they are a distraction to drivers and as a consequence may actually increase the road death toll. This paper presents a detailed evaluation of the impact of 62 fixed cameras on 30mph roads in the UK. The changes in accident frequency attributable to the effect of cameras on both speed and route choice are separately estimated, as are the changes due to general trends in accidents and regression-to-mean: changes that would have occurred with or without a camera. It was found that the average effect of the cameras was a fall in personal injury accidents of some 25%, of which a fall of some 20% was attributable to their impact on speed with a 5% fall due to diversion of traffic away from routes with cameras. Safety improvements were observed over a distance of up to 1km upstream and downstream of the cameras, with no evidence that any sudden changes in speed on approaching or passing the cameras had any detrimental effect on safety. The impact of the cameras on fatal and serious accidents was rather less certain and served to highlight the dangers of ignoring regression-to-mean effects. While reductions of the order of one third were observed, more than half of this was attributable to regression-to-mean. It is argued that the current guidelines for locating cameras, and the criteria for judging their success or failure, place too much emphasis on observed numbers of fatal and serious accidents: without proper controls for regression-to-mean some roads which might benefit from increased speed enforcement may not qualify for a camera while cameras which are actually improving road safety may be judged unsuccessful. (A)

A detailed evaluation of the impact of speed cameras on safety

The sensitivity of estimates of regression to the mean

The problem of the optimisation of traffic signals in a network comes in a variety of forms, depending on whether the traffic model used to evaluate any proposed set timings is deterministic or Monte Carlo, whether the drivers’ routes are fixed or dependent on the signal timings, and whether the control is fixed-time or responsive. The paper deals with fixed-time control, and investigates the application of the cross-entropy method (CEM) to find the global optimum solution. It is shown that the CEM can be applied both to deterministic and Monte Carlo problems and to fixed-route or variable-route problems. Such combinatorial problems typically have a large number of local optima and therefore simple hill-climbing methods are ineffective. The paper demonstrates firstly how the cross-entropy method provides an efficient and robust approach when the traffic model that provides, for any solution x , the value of the performance index (PI) z ( x ) is deterministic. It then goes onto discuss the effect of noise in the evaluation process, such as arises when a Monte Carlo simulation model is used, so that the PI can be expressed as z ( x ) =  z 0 ( x ) +  e ( x ) where e is a random error, whose variance s 2 depends inversely either on T , the length of the simulation run, or on M the number of simulation runs carried out for any solution. A second example illustrates the application of the CEM to a noisy problem, in which a Monte Carlo traffic assignment model is used to estimate drivers’ route choices in response to any proposed signal timings, and shows the principles by which the values of either T or M must be adapted through the iterative process.

Mike Maher

Papers

Calibration of second order traffic models using continuous cross entropy method

Child pedestrian casualties and deprivation

A detailed evaluation of the impact of speed cameras on safety

The sensitivity of estimates of regression to the mean

Signal optimisation using the cross entropy method