Impact of Dynamic and Safety-Conscious Route Guidance on Accident Risk

Abdulhai, Baher; Look, Horace

doi:10.1061/(asce)0733-947x(2003)129:4(369)

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…To facilitate the presentation of the essential ideas without loss of accuracy or leading to an erroneous conclusion, the example network used by Bell et al, [26] is adopted in this paper as shown in Figure 3. This example network consists of 17 links, 1 OD pair (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), and 10 paths. The nodes 5, 6, 7, and 8 in the example network denote the signal-controlled intersections.…”

Section: Numerical Examplementioning

confidence: 99%

“…In Figure 3, the numbers outside and inside the brackets beside each link are the corresponding link numbers and the free-flow link travel times (minute), respectively. The potential accident rates of left, right turns, and through traffic for passing through an intersection are calculated on the basis of the functions developed by Abdulhai and Look [10]. These functions are calibrated to estimate the expected number of accidents per hour for different turning movements based on the corresponding turning flow per hour.…”

Section: Numerical Examplementioning

confidence: 99%

“…Traffic accidents at road intersections place a huge burden on society [9]. The risk of accidents on urban roads was found to be highly related to the number of turns at road intersections [10]. In Ontario, the overall within-intersection and intersection-related collisions constituted 45 and 44.7% of all collisions in 1995 and 1997, respectively [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In Ontario, the overall within-intersection and intersection-related collisions constituted 45 and 44.7% of all collisions in 1995 and 1997, respectively [11,12]. Turning traffic conflict at intersections is one of the major sources of accidents [10]. Some route guidance systems attempt to recommend the routes with the minimal number of turns to drivers.…”

Section: Introductionmentioning

confidence: 99%

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A risk‐averse user equilibrium model for route choice problem in signal‐controlled networks

Lam

Chan

et al. 2010

J of Advced Transportation

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SUMMARYThis paper proposes a new risk-averse user equilibrium (RAUE) model to estimate the distribution of traffic flows over road networks with taking account the effects of accident risks due to the conflicting traffic flows (left-and right-turning and through traffic flows) at signalized intersections. It is assumed in the proposed model that drivers consider simultaneously both the travel time and accident risk in their route choices. The accident risk of a route is measured by the potential accident rate on that route. The RAUE conditions are formulated as an equivalent path-based variational inequality problem which can be solved by a path-based solution algorithm. It is shown that the traditional user equilibrium (UE) model is in fact a special case of the proposed model. A numerical example on a grid network is used to illustrate the application of the proposed model and to compare the results with the conventional UE traffic assignment. Numerical results show that the traditional UE model may underestimate the total system travel time and overestimate the system accident rate. Sensitivity tests are also carried out to assess the effects of drivers' preferences, signal control parameters (i.e., green time proportions), and various network demand levels on the route choice problem.

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Section: Numerical Examplementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A risk‐averse user equilibrium model for route choice problem in signal‐controlled networks

Lam

Chan

et al. 2010

J of Advced Transportation

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“…There have been some examples of incorporating accident risk into vehicles' routing choices [7]- [9]. However, this work has either relied on static (or only slowly-changing) accident risk models, global knowledge across the road network, or both.…”

mentioning

confidence: 99%

On road network utility based on risk-aware link choice

Fitzgerald

Landfeldt

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-We have previously shown how it is possible to lower accident risk levels while at the same time increasing traffic flows with the aid of vehicular ad-hoc networks. In this paper, we extend the online traffic accident risk mitigation system to include link choice. We develop an algorithm that allows vehicles to choose road links that provide the current maximal utility, based on information received over a vehicular network, whilst remaining below acceptable risk levels. Additionally, we investigate how to improve the performance of this algorithm by considering the effect a vehicle's link choice has on the utility of other vehicles. We introduce a social penalty for reducing other vehicles' utility and investigate the effects of including this as a factor in vehicles' link choices. The resulting system sheds new light on the problem of effective link choice by route selection algorithms and highlights the potential that exists in VANET-based traffic management.

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