A Data Mining Approach to Identify Key Factors of Traffic Injury Severity

Seventy percent of the traffic crash fatalities of Iran happen on rural roads, and a significant proportion of the rural roads network of this country is constituted of the main two-lane, two-way roads. The purpose of this study is to identify the most important factors which affect injury severity of drivers involved in traffic crashes on these roads, so that by eliminating or controlling such factors an overall safety improvement can be accomplished. Using the Classification and Regression Tree (CART), one of the powerful data mining tools, the crash data pertaining to the last three years (2006-2008) were analyzed. The variable selection procedure was carried out on the basis of Variable Importance Measure (VIM) which is one of the CART method outputs. The results revealed that not using the seat belt, improper overtaking and speeding are the most important factors associated with injury severity. KEYWORDS: injury severity; traffic safety; data mining; Classification and Regression Trees (CART); Variable Importance Measure (VIM

A control and surveillance ITS location model to improve safety

Traffic safety in rural highways can be considered as a constant source of concern in many countries. Nowadays, transportation professionals widely use Intelligent Transportation Systems (ITS) to address safety issues. However, compared to metropolitan applications, the rural highway (non-urban) ITS applications are still not well defined. This paper provides a comprehensive review on the existing ITS safety solutions for rural highways. This research is mainly focused on the infrastructure-based control and surveillance ITS technology, such as Crash Prevention and Safety, Road Weather Management and other applications, that is directly related to the reduction of frequency and severity of accidents. The main outcome of this research is the development of a ‘ITS control and surveillance device locating model’ to achieve the maximum safety benefit for rural highways. Using cost and benefits databases of ITS, an integer linear programming method is utilized as an optimization technique to choose the most suitable set of ITS devices. Finally, computational analysis is performed on an existing highway in Iran, to validate the effectiveness of the proposed locating model

An approximate reliability evaluation method for improving transportation network performance

Considering the importance of maintaining network performance at desired levels under uncertainty, network reliability, as a new approach to assessing the performance of degradable urban transportation networks, has become increasingly developed in two recent decades. In this paper, a method for optimizing resource allocation to meet the required levels of transportation network reliability is proposed. The worked out method consists of two stages: at stage one, a method for computing the reliability of network connectivity based on the reliability of computing arc performance with an assumption that capacities are random variables for each arc is presented. These random variables are assumed to be conformed to especial probability density functions which can be modified through investing to improve the performance reliability of the arcs. At stage two, a mixed integer nonlinear programming model is developed to optimize resource allocation in the network. Numerical results are also provided in a simple network to demonstrate the capability of the employed method. First published online: 27 Oct 201

Driver behaviour among professional taxi and truck drivers: Light passenger cars versus heavy goods vehicles

Professional drivers are drivers whose profession is to drive a vehicle such as truck and taxi for working purposes. However, these drivers constitute a heterogeneous group and generalizing assumptions about risky driving behaviour across the entire group might be misleading. The current study aimed to investigate similarities and differences of self-reported risky driving behaviour and crash involvement among different groups of professional drivers. Two rather large samples of taxi drivers obtained from 20 taxi stations in two cities (n = 381) and heavy goods vehicles (truck) drivers obtained from a roadside survey in 10 provinces (n = 785) completed the same 27-item Driver Behaviour Questionnaire (DBQ) in Iran. Principal component analysis showed that the DBQ segmented into four dimensions both among taxi and truck drivers. Further, a multi-group confirmatory factor analysis (MGCFA) supported strong measurement invariance in the DBQ factor structure across the two samples. The results showed that taxi drivers were more likely than truck drivers to commit errors as well as ordinary and aggressive violations. A one unit increase in ordinary and aggressive violations increased the probability of having experienced a traffic crash in the last year by 69% and 98% for taxi drivers, respectively, and 37% and 42% among truck drivers, respectively. This highlights that driving violations increased the probability of crash involvement almost twice as much among taxi drivers compared to truck drivers. Policymakers could target ordinary and aggressive violations by establishing better driving training and by improving the licensing procedures among professional drivers

A Reliability-Based Resource Allocation Model for Transportation Networks Affected by Natural Disasters

This paper is concerned with the development of a resource allocation model for road networks under supply uncertainty caused by natural disasters. An optimization model is proposed to determine which links should be invested for the system to perform better while encountering natural disasters such as earthquake. The connectivity reliability and travel time reliability of origin-destinations (ODs) are selected as performance measures to do so. The Monte-Carlo simulation method is used to estimate the reliability measures and the model is solved by the genetic algorithm. The proposed model is implemented on a test network to demonstrate the results

Application of imperialist competitive algorithm to the emergency medical services location problem

Minimization of arrival time at scenes plays an essential role to help injured people in emergency events. This can be undertaken through mathematical programming models, called emergency medical services location problem, and solved by conventional exact algorithms or by recent meta-heuristic methods as well. Meta-heuristic algorithms have recently been realized to be more efficient in the sense of computing times especially in large-scale cases. The emergency medical services location problem would be further complicated when the number of stations and/or emergency vehicles, as an important indicator of system costs, should be determined at the same time. In this paper, a newly introduced optimization method, Imperialist Competitive Algorithm (ICA), is used to solve the EMS location problem. The ICA mimics the human's socio-political evolution to solve continuous problems. In this paper, a discrete version of the ICA is sought to be adapted to solve the EMS location problem. The adapted ICA algorithm is then applied on two benchmark problems with four different demand scenarios as well as on the real transportation network of Mashhad City. Results of this algorithm are compared with those of other wellknow meta-heuristic algorithms (i.e. the genetic algorithm, the simulated annealing and the particle swarm optimization). These results indicate that the cpu time of the ICA is averagely less than that obtained from the other algorithms, and the number of required ambulances is not considerably different

Travel Time Reliability Measures Accommodating Scheduling Preferences of Travelers

Existing travel time reliability measures fail to accommodate scheduling preferences of travelers and cannot distinguish between the variability associated with early and late arrivals. This study introduces two new travel time reliability measures based on concepts from behavioral economics. The first proposed measure is an indicator of the width of travel time distribution. It considers scheduling preferences of travelers and can distinguish between early arrival and late arrival. The second measure determines the skewness of travel time distribution. To estimate the proposed measures, travel time is modeled by mixture models and closed-form expressions are derived for the expected values of early and late arrivals. In addition, real travel time data from a freeway segment is used to compare the proposed measures with the existing travel time reliability measures. The results suggest that, although there exist significant correlations between travel time reliability measures, travelers’ preferences have considerable effects on the travel time reliability as perceived by them. Furthermore, four measures are developed based on the notions of early and late arrivals to assess the on-time performance (schedule adherence) of transit vehicles at stop level. The results of this study show that the four measures can serve as complementary to the existing on-time performance indices

Analysis of factors associated with traffic injury severity on rural roads in Iran

BACKGROUND: Iran is a country with one of the highest rates of traffic crash fatality and injury, and seventy percent of these fatalities happen on rural roads. The objective of this study is to identify the significant factors influencing injury severity among drivers involved in crashes on two kinds of major rural roads in Iran: two-lane, two-way roads and freeways. METHODS: According to the dataset, 213569 drivers were involved in rural road crashes in Iran, over the 3 years from 2006 to 2008. The Classification And Regression Tree method (CART) was applied for 13 independent variables, and one target variable of injury severity with 3 classes of no-injury, injury and fatality. Some of the independent variables were cause of crash, collision type, weather conditions, road surface conditions, driver's age and gender and seat belt usage. The CART model was trained by 70% of these data, and tested with the rest. RESULTS: It was indicated that seat belt use is the most important safety factor for two-lane, two-way rural roads, but on freeways, the importance of this variable is less. Cause of crash, also turned out to be the next most important variable. The results showed that for two-lane, two-way rural roads, "improper overtaking" and "speeding", and for rural freeways, "inattention to traffic ahead", "vehicle defect", and "movement of pedestrians, livestock and unauthorized vehicles on freeways" are the most serious causes of increasing injury severity. CONCLUSIONS: The analysis results revealed seat belt use, cause of crash and collision type as the most important variables influencing the injury severity of traffic crashes. To deal with these problems, intensifying police enforcement by means of mobile patrol vehicles, constructing overtaking lanes where necessary, and prohibiting the crossing of pedestrians and livestock and the driving of unauthorized vehicles on freeways are necessary. Moreover, creating a rumble strip on the two edges of roads, and paying attention to the design consistency of roads can be a helpful factor in order to prevent events such as "overturning" and improve the overall safety of freeways

The impact of irregular headways on seat availability

This paper introduces seat availability reliability, a new reliability measure that assesses the performance of transit lines. This alternative reliability indicator is defined in terms of the coefficient of variation, which examines the relative deviation of seat availability. An analytical approach is utilized based on the conditional expectation to achieve relationships for the mean and variance of the number of boarding and alighting passengers and seat availability at stops. Furthermore, confidence intervals for seat availability are derived. It is proposed that such information might be useful for traveler information systems. The proposed model is applied to a schematic transit line to show how sensitive seat availability is to headway variation (irregular dispatching) and demand uncertaint