Yc Li

Full Bayesian Method for the Development of Speed Models: Applications of GPS Probe Data

AbstractTraffic speed is one of the basic variables that indicates the level of service of a road entity. It plays an essential role in transportation planning and management. This study attempts to establish a prediction model for speed distribution, in terms of average travel speed and standard deviation, using probe vehicle data in Hong Kong. Taking advantage of detailed traffic flow data obtained from the annual traffic census, a comprehensive traffic information database can be established using the geographical information system technique. The effects of traffic flow, road geometry, and weather conditions on speed distribution are determined using the Markov-chain Monte Carlo (MCMC) simulation approach full Bayesian method.

Public transport policy measures for improving elderly mobility

Abstract Population aging is happening in most of the worlds metropolitan cities, and the proportion of elderly adults is predicted to increase significantly in the coming decades. This rapid growth of elderly populations may lead to serious transport issues when their mobility is compromised by the unavailability of public transport services. Public transport concession fare schemes are commonly implemented in many cities to encourage the elderlys participation in social activities. However, these policies emphasize the role of money (i.e., travel fares) in determining willingness to travel. Other possible factors, such as walking distance to and from stops and stations, wait times for public transport services, and seat availability, have not been considered by transport operators and policy makers. In this study, we interviewed 613 elderly Hong Kong residents aged 60 or above regarding their travel decisions using designated modes of public transport to attend social activities in four hypothetical games. A total of 2452 observations were collected for model development. Binary logistic regression models were calibrated to determine which factors significantly influenced the elderlys travel decisions. Based on the model results, this paper suggests policy measures to strengthen public transport planning in Hong Kong with the goal of improving elderly mobility. The findings provide policy insights that can also be applied to other metropolitan cities with similar traffic conditions.

Effective measures for combating drink-driving offenses: an attitudinal model for Hong Kong

In Hong Kong, legal limits on the concentration of alcohol permitted in drivers’ blood, urine, and breath were introduced in 1995. Later legislation empowered the police to conduct random breath tests (RBTs) without the need for suspicion. Although drink-driving accounts for a relatively small portion of the traffic accidents in Hong Kong, the average killed and seriously injured rate for drink-driving accidents is higher than that for overall traffic accidents. From time to time, there are calls for heavier penalties such as longer prison sentences and driving disqualification periods for drink drivers, particularly those who cause severe injury and death. However, no consensus has been reached on the actual effectiveness of severe penalties in combating drink-driving offenses. In this study, a self-administrated, mail-back questionnaire including six stated preference games was conducted to evaluate drivers’ perceptions of the current levels of penalties against drink-driving offenses. The game measured the associations between the propensity to drive after drinking and penalty levels. The results of a mixed logistic regression model revealed that the presence of an RBT checkpoint and an increase in driving-offense points and the durations of licence disqualification and imprisonment correlated positively with an increase in the deterrent effects of measures taken to combat drink driving.

Estimation of Scaling Factors for Traffic Counts Based on Stationary and Mobile Sources of Data

To combine mobile sources and stationary sources, a modeling approach to quantify the variability of the linear projection function using a non-linear regression method is established in this study. Weights that vary spatial-temporally are assigned to neighboring scaling factors. Together with a normalized weighted average function, the subject scaling factor is determined. The framework is applied to a case study in Hong Kong combining Global Positioning System data and the annual traffic counts from 85 fixed stations in Annual Traffic Census database. The performance of the models is assessed based on relative root mean square error and Akaike information criterion.

Effects of drink driving on crash risk based on random breath test data

Introduction Driving under the influence of alcohol (DUIA) is an important contributor to road crashes, injuries, and fatalities. Legal limits on drivers breath alcohol concentration (BrAC) were first introduced in Hong Kong during the 1990s. Since then, the number of alcohol-related crashes has decreased. In 2009, police were empowered to conduct random breath tests (RBT) at roadblocks at any time. The availability of comprehensive RBT data allows us to determine the relationship between crash risk and BrAC, while controlling for confounding factors such as temporal distribution, road environment, and traffic conditions. Method In this study, we established two negative binomial regression models to analyse the risk of crashes in which people are killed or seriously injured (KSI crashes) and those involving only slight injuries on 182 urban road segments. Results Our results indicated that a higher mean BrAC markedly increases the risk of KSI crashes. However, there was no relationship between the risk of crashes involving slight injury and mean BrAC, although the absence of a hard shoulder, the presence of roadside parking bays, the dawn period, and the location noticeably increase the risk of these crashes. Conclusion It is worth exploring the benefits of remedial measures if comprehensive information on demographics and the driving habits of individual drivers become available in the future.

A simulation study of the effects of alcohol on driving performance in a Chinese population

UNLABELLEDnDriving under the influence of alcohol (DUIA) is a significant factor contributing to road traffic crashes, injuries, and fatalities. Although the effects of alcohol on driving performance are widely acknowledged, studies of the effects of alcohol impairment on driving performance and particularly on the control system of Chinese adults are rare. This study attempts to evaluate the effects of alcohol on the driving performance of Chinese adults using a driving simulator.nnnMETHODnA double-blind experimental study was conducted to evaluate the effects of alcohol impairment on the driving performance of 52 Chinese participants using a driving simulator. A series of simulated driving tests covering two driving modules, including emergency braking (EB) and following braking (FB), at 50km/h and 80km/h were performed. Linear mixed models were established to evaluate driving performance in terms of braking reaction time (BRT), the standard deviation of lateral position (SD-LANE), and the standard deviation of speed (SD-SPEED).nnnRESULTSnDriving performance in terms of BRT and SD-LANE was highly correlated with the level of alcohol consumption, with a one-unit increase in breath alcohol concentration (BrAC) degrading BRT and SD-LANE by 0.3% and 0.2%, respectively. Frequent drinkers generally reacted faster in their BRT than less-frequent drinkers and non-drinkers by 10.2% and 30.6%, respectively. Moreover, alcohol impairment had varying effects on certain aspects of the human control system, and automatic action was less likely to be affected than voluntary action from a psychological viewpoint.nnnCONCLUSIONnThe findings should be useful for planning and developing effective measures to combat drink driving in Chinese communities.

Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model

This study aimed to identify the factors affecting the crash-related severity level of injuries in taxis and quantify the associations between these factors and taxi occupant injury severity. Casualties resulting from taxi crashes from 2004 to 2013 in Hong Kong were divided into four categories: taxi drivers, taxi passengers, private car drivers and private car passengers. To avoid any biased interpretation caused by unobserved spatial and temporal effects, a Bayesian hierarchical logistic modeling approach with conditional autoregressive priors was applied, and four different model forms were tested. For taxi drivers and passengers, the model with space-time interaction was proven to most properly address the unobserved heterogeneity effects. The results indicated that time of week, number of vehicles involved, weather, point of impact and driver age were closely associated with taxi drivers injury severity level in a crash. For taxi passengers injury severity an additional factor, taxi service area, was influential. To investigate the differences between taxis and other traffic, similar models were established for private car drivers and passengers. The results revealed that although location in the network and driver gender significantly influenced private car drivers injury severity, they did not influence taxi drivers injury severity. Compared with taxi passengers, the injury severity of private car passengers was more sensitive to average speed and whether seat belts were worn. Older drivers, urban taxis and fatigued driving were identified as factors that increased taxi occupant injury severity in Hong Kong.

Special issue on “Driving Simulation Experiments and Behavior Analyses”

Driving simulators were introduced in the 1970s, providing a promising approach to studying driving performance, traffic safety, and road infrastructure design in an inherently safe environment. Nevertheless, their development was limited by the slow and complex systems for image processing and modelling at that time. Fortunately, motivated by general advances in technology, simulator-based experiments have become more sophisticated in recent years. Simulation experiments cover a wide variety of areas, including driver behavior and human factors, driver perception and modelling, driver distraction, impaired driving performance, infrastructure design, innovative data collection and analyses, design of simulator scenarios, and simulator sickness. Each area deserves substantial research in its own right, providing state-of-the-art solutions for road traffic concerns and implications for future development. This special issue of Journal of Transportation Safety & Security is intended to provide a coherent overview of current simulator-based research into the latest aspects of transportation safety and security and their future developments. It contains eight articles that cover a number of the topics raised above. Due to the increasing use of mobile phones, a considerable number of studies have analyzed the driver distraction caused by phone use while driving. In the work presented here, Calvi and colleagues analyzed the effect of mobile phone conversations on the driving performance variables of reaction time and speed under different road configurations and geometries during a critical driving situation. A fixed-base driving simulator was used to evaluate driver reaction time and speed, specifically in reaction to the unexpected braking of a leading vehicle. The main findings were that drivers generally drove at reduced speeds while making phone calls, while the reaction time generally increased, especially under certain driving conditions. In addition, the limitations of this simulator-based experiment are also discussed to conclude this article. Drivers’ speed selection has been a particular focus of road safety research, as wide speed distributions have been found to increase the crash risk. To explore drivers’ speed choices under the influences of infrastructure, traffic, risk versus benefit, and driver demographics, Sadia and colleagues present a study using the STISIM driving simulator. A supplementary stated-preference survey was used to evaluate the relationship of the drivers’ behavior and speed selection in the simulator to their demographic characteristics, subjective