Identifying psychological and socio-economic factors affecting motorcycle helmet use

Sixty percent of motorcyclist fatalities in traffic accidents of Iran are due to head injuries, but helmet use is low, despite it being a legal requirement. This study used face-to-face interviews to investigate the factors associated with helmet use among motorcycle riders in Mashhad city, the second largest city in Iran. Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) were used for data reduction and identification of consistent features of the data. Ordered and multinomial logit analyses were used to quantify the influences on helmet use and non-use. The data show that 47% of the sample used a helmet use, but a substantial proportion of these did not wear their helmet properly. In addition, 5% of motorcyclists believed that helmets reduced their safety. Norms, attitudes toward helmet use, risky traffic behavior and awareness of traffic rules were found to be the key determinants of helmet use, but perceptions of enforcement lacked influence. Duration of daily motorcycle trips, riding experience and type of job also affected helmet use. Results indicate that motorcyclist training, safety courses for offending motorcyclists and social programs to improve social norms and attitudes regarding helmet use are warranted, as are more effective law enforcement techniques, in order to increase proper use of helmets in Iranian motorcyclists. In addition, special safety courses should be considered for motorcyclists who have committed traffic violations

A Direct Demand Model of Departure Time and Mode for Intercity Passenger Trips

Travel demand is well announced as a crucial component of transportation planning. This paper aims to develop a direct demand model, denoting a more acceptable abstraction of reality, for intercity passengers in daily work and leisure trips in Tehran province. The model utilizes combined estimation across the data source, collected in 2011, of travelers originating from the city of Tehran and heading toward two destination clusters: intra-province and inter-province. The paper sketches a way to predict simultaneous choice of departure time and travel mode under the influence of zonal (origin, destination, and residence), individual and household socio-demographic, and trip-related variables. The time frame for analysis of departure time is [5-19] and available modes are auto, taxi, bus, and metro. Multinomial Logit (MNL) and Nested Logit (NL) models as behavioral models are selected from discrete choice family to provide appropriate direct demand structure. Besides, the paper discusses Independent Irrelative Alternative (IIA) assumption of the models and demonstrates choice order of NL; Travelers choose departure time prior to mode at first level and then decide on mode at second level. Finally, travel demand elasticity and marginal effect with respect to travel time, age, and auto cost are also highlighted

Final analytical comparison of aggregate and disaggregate mode choice models transferability

ABSTRACT: Transportation models as tools for transportation planning are critical to such related decisions. Considering the high cost of calibrating and validating such models, effective alternatives are highly sought for; one such alternative being the use of models calibrated for other cities. This calls for transferability analysis which has not been the subject of many researches. Due to criticality of aggregate and disaggregate data in transportation models, this paper tries to compare transferability of models calibrated with data of both groups. Mode choice models for daily work trips in two real-sized cities of Qazvin and Shiraz are analyzed. Models are calibrated employing multinomial logit structure with four modes of private car, taxi, bus, and 2-wheelers. In order to increase reliability of results, the top five best models are selected for each city-data category to be transferred. Based on transferability test statistics, transfer index, and goodness-of-fit of transfer models, aggregate models are not transferable and their results are deceptive. Transferability measures of these models are not in acceptable range; whereas transferability of disaggregate models have relative proper response. According to transfer index and goodness-of-fit of origin models operate similar to destination models. However transferability test statistics rejects the assumption of equality coefficients in both cities models. Using personal variables helps to effectively transfer origin models in addition to improve them

A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases

A joint model of destination and mode choice for urban trips: a disaggregate approach

Trip destination and mode choice are highly influenced by travelers\u27 perceptions and behaviors; selecting a destination and a vehicle for a trip are two interdependent problems. This paper presents and applies a disaggregate joint model for traveler destination and mode choice. The choice model uses fuzzy set and probability theory to deal with the uncertainty embedded in travelers\u27 perceptions and behaviors. The model is structured as a decision tree in which the fuzzy and non-fuzzy classification of influential variables regarding destination selection and mode choice expand the tree. The most influential explanatory variables among all the variables categorized for travelers\u27 household, trip, and living zone specifications are selected based on the maximizing information. An aggregation method is designed to provide aggregate estimates for transportation planning based on the suggested disaggregate choice model. A data-set of over 9000 home-based morning peak-hour trips in Shiraz, a large city in Iran, is used for model construction and evaluation. When compared with a multinomial logit (MNL) model, the suggested models\u27 estimates are more accurate than the traditional MNL model

A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip

A path-based greedy algorithm for multi-objective transit routes design with elastic demand

This paper is concerned with the problem of finding optimal sub-routes from a set of predefined candidate transit routes with the objectives of maximizing transit ridership as well as minimizing operational costs. The main contributions of this paper are: (1) considering transit ridership maximization in a multi-objective bi-level optimization framework; (2) proposing a greedy algorithm for the multi-objective design problem; (3) applying an efficient path-based algorithm to solve the lower level multi-modal traffic assignment problem. Numerical experiments indicate that the proposed algorithm is not only able to approximate the Pareto-optimal solutions with satisfactory accuracy, but also achieves a fast performance even for problems of real-world scale

Impact of Carpooling on Fuel Saving in Urban Transportation: Case Study of Tehran

AbstractApproximately 40 percent of fuel consumption in large cities is related to transportation. A noticeable amount of fuel is wasted due to traffic congestion in peak hours. Transportation planners look for policies to reduce congestion to save fuel and increase energy efficiency. One of the policies is carpooling that emphasizes on a shared use of private cars. In this paper, the factors which persuade travellers to choose carpooling are investigated for Tehran city, capital of Iran. A stated preferences (SP) survey has been used to observe travellers’ tendency of carpooling. SP is a survey technique which mathematically shows the preferences, based on people's stated choices and their responses to hypothetical situations. The survey questionnaires filled out by 470 travellers used their own automobiles. Considering the data, carpooling impacts are analyzed in different situations. In this approach a demand function is calibrated and utilized to predict percent of travellers choose carpooling. When all interested travellers, independence of knowing appropriate rideshare or not, choose carpooling then vehicle trips per day would decrease about 780000 vehicle trips per day and reduce annual fuel consumption by 336.53 million litres. The results show that if appropriate strategies like carpooling websites are designed to help travellers for identifying appropriate rideshares, carpooling would increase by 30 percent and this increase will reduce annual fuel consumption about 240 million litres. Results also show that high occupancy vehicle lanes (HOV) that reduce travel time for ridesharing may not highly influence on carpooling tendency of travellers

Driver Risk Assessment Model Considering Trip Characteristics Using Insurance Data System

AbstractRisky drivers impose a lot of damages to insurance companies, thus insurance providers usually may offer high-risk drivers coverage with higher prices. High-risk drivers are identified in terms of violations and accidents history. This paper investigates the impact of drivers’ trip characteristics in addition to personal and socio-economic characteristics on drivers’ riskiness. In case of non-accessibility to the history of drivers’ violations or accidents, the impact model can be helpful for insurance providers to measure drivers’ risk-taking attitudes. Then, high-risk driver coverage will be more expensive than the standard coverage. Insurance data from ASIA, the largest auto insurance company in Iran, for 506 drivers randomly selected are obtained using ASIA data system and interviewing with drivers at ASIA insurance claim centres. An ordered logit model has been utilized as driver risk assessment model. The dependent variables ware the number of accidents on insurance records and traffic ticket. The results show that drivers often use private vehicles for non-mandatory purposes are riskier than mandatory purposes. Furthermore, drivers usually traveling on rural roads are high-risk for drivers comparing to urban roads. The findings show that insurance providers may suggest expensive coverage for rural road drivers with non-mandatory purposes