Zuduo Zheng

Impact of mobile phone use on car-following behaviour of young drivers

Multitasking, such as the concurrent use of a mobile phone and operating a motor vehicle, is a significant distraction that impairs driving performance and is becoming a leading cause of motor vehicle crashes. This study investigates the impact of mobile phone conversations on car-following behaviour. The CARRS-Q Advanced Driving Simulator was used to test a group of young Australian drivers aged 18-26 years on a car-following task in three randomised phone conditions: baseline (no phone conversation), hands-free and handheld. Repeated measure ANOVA was applied to examine the effect of mobile phone distraction on selected car-following variables such as driving speed, spacing, and time headway. Overall, drivers tended to select slower driving speeds, larger vehicle spacings, and longer time headways when they were engaged in either hands-free or handheld phone conversations, suggesting possible risk compensatory behaviour. In addition, phone conversations while driving influenced car-following behaviour such that variability was increased in driving speeds, vehicle spacings, and acceleration and decelerations. To further investigate car-following behaviour of distracted drivers, driver time headways were modelled using Generalized Estimation Equation (GEE). After controlling for various exogenous factors, the model predicts an increase of 0.33s in time headway when a driver is engaged in hands-free phone conversation and a 0.75s increase for handheld phone conversation. The findings will improve the collective understanding of distraction on driving performance, in particular car following behaviour which is most critical in the determination of rear-end crashes.

Human Factors in Modelling Mixed Traffic of Traditional, Connected, and Automated Vehicles

Connected and automated vehicle technologies are widely expected to revolutionize transport systems, enhancing the mobility and quality of life while reducing the environmental impact. However, in the foreseeable future, connected and automated vehicles will have to co-exist with traditional vehicles, indicating a great research need of modelling mixed traffic flow. In few attempts of modelling mixed traffic flow recently, human factors are largely ignored, despite their critical roles in understanding traffic flow dynamics and effective operation and control of this mixed traffic flow. To properly investigate the role of human factors in mixed traffic, we have designed a series of experiments using a high-fidelity driving simulator. Complementary information is collected using questionnaires. This study can assist in developing accurate, realistic, and robust microscopic traffic flow models.

Survey and Analysis of Driver’s Response to Dynamic Traffic Information

Even the most sophisticated Traffic Information Systems (TIS) might not be successful if driver’s response to traffic information cannot be estimated in advance. Overreaction and concentration are often triggered if the estimations of driver’s response to the information are not correct. Unfortunately, the analysis of drivers’ response to traffic information is also very complex, because the responses of different drivers can be totally different to the same information. This paper presents the results of a study on driver’s response to and evaluation of dynamic traffic information from radio and Variable Message Signs (VMS) using qualitative and statistic methods. The analysis of the survey conducted in Changchun city shows that the usefulness of traffic information from radio is admitted by most drivers and that this kind of information has a significant influence on the drivers’ behavior. The analysis also shows that VMS are not dangerous to drivers if designed carefully. The analysis also revealed that congestion information and alternative route recommendations are most welcomed in general regardless of the forms of media used.

“Privilege to Kill” Phenomenon on Developing Countries' Roads: A Preliminary Case Study of China

This study aims to examine the severe consequences of traffic crashes related to a special group of drivers in China, which is called the “Second Rich & Powerful Generation” (SRPG). The unique driving behaviors and attitudes of this special group are intertwined with the general cultural and social environment in China. To investigate the difference of traffic crash consequences between drivers who belong to SRPG and general driver population, injuries and fatalities in 2009, 2010, 2011 and 2012 were compared. Results consistently showed that while no significant difference was detected between these two groups in terms of injuries, fatalities per crash caused by SRPG were significantly larger than that caused by general driver population. Findings from our study clearly demonstrate the complexity of road safety issues in developing countries, and can also be used to develop road safety improvement strategies tailored to SRPG.

Shrinking Neighborhood Evolution--a novel stochastic algorithm for numerical optimization

In this paper we develop and test a novel stochastic algorithm SNE (Shrinking Neighborhood Evolution) based on the issue of bound constrained optimization problem. Its heuristic strategy is simple and direct-related to the search region of the solving problem based on the concept of “k-box-neighborhood” -defined in this paper. Our numerical experiments show that the optimization capability of SNE is competing to other congeneric algorithms such as Particle Swarm Optimizer (PSO), Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) and Differential Evolution (DE). The new method requires few control parameters, easy to use, and has promising potentials to parallel computation.

User satisfaction with train fares: A comparative analysis in five Australian cities

In the public transport industry, travellers’ perceived satisfaction is a key element in understanding their evaluation of, and loyalty to ridership. Despite its notable importance, studies of customer satisfaction are under-represented in the literature, and most previous studies are based on survey data collected from a single city only. This does not allow a comparison across different transport systems. To address this underrepresentation, this paper reports on a study of train passengers’ satisfaction with the fare paid for their most recent home-based train trip in five Australian capital cities: Sydney, Melbourne, Brisbane, Adelaide, and Perth. Two data sources are used: a nation-wide survey, and objective information on the train fare structure in each of the targeted cities. In particular, satisfaction with train fares is modelled as a function of socio-economic factors and train trip characteristics, using a random parameters ordered Logit model that accounts for unobserved heterogeneity in the population. Results indicate that gender, city of origin, transport mode from home to the train station, eligibility for either student or senior concession fare, one-way cost, and waiting time as well as five diverse interaction variables between city of origin and socio-economic factors are the key determinants of passenger satisfaction with train fares. In particular, this study reveals that female respondents tend to be less satisfied with their train fare than their male counterparts. Interestingly, respondents who take the bus to the train station tend to feel more satisfied with their fare compared with the rest of the respondents. In addition, notable heterogeneity is detected across respondents’ perceived satisfaction with train fare, specifically with regard to the one-way cost and the waiting time incurred. An intercity comparison reveals that a city’s train fare structure also affects a traveller’s perceived satisfaction with their train fare. The findings of this research are significant for both policy makers and transport operators, allowing them to understand traveller behaviours, and to subsequently formulate effective transit policies.

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

Merging behavior is inevitable at on-ramp bottlenecks and is a significant factor in triggering traffic breakdown. In modeling merging behaviors, the gap acceptance theory is generally used. Gap acceptance theory holds that when a gap is larger than the critical gap, the vehicle will merge into the mainline. In this study, however, analyses not only focus on the accepted gaps, but also take the rejected gaps into account, and the impact on merging behavior with multi-rejected (more than once rejecting behavior) gaps was investigated; it shows that the multi-rejected gaps have a great influence on the estimation of critical gap and merging prediction. Two empirical trajectory data sets were collected and analyzed: one at Yan’an Expressway in Shanghai, China, and the other at Highway 101 in Los Angeles, USA. The study made three main contributions. First, it gives the quantitative measurement of the rejected gap which is also a detailed description of non-merging event and investigated the characteristics of the multi-rejected gaps; second, taking the multi-rejected gaps into consideration, it further expanded the concept of the “critical gap” which can be a statistic one and the distribution function of merging probability with respect to such gaps was analyzed by means of survival analysis. This way could make the full use of multi-rejected gaps and accepted gaps and reduce the sample bias, thus estimating the critical gap accurately; finally, considering multi-rejected gaps, it created logistic regression models to predict merging behavior. These models were tested using field data, and satisfactory performances were obtained.

Worker Attitude Toward Bus Rapid Transit: Considering Dhaka, Bangladesh

The government of Bangladesh is planning to develop and implement bus rapid transit (BRT) in Dhaka City. This paper presents a stated choice survey conducted to understand workers’ attitudes toward BRT in Dhaka. Survey data were analyzed with a multinomial logit model to scrutinize the effect of social and economic factors on participants’ mode choices. Analysis results revealed that males, older workers, workers with higher education qualifications, and workers with higher incomes have a greater tendency toward choosing BRT.