The impact of the connected environment on driving behavior and safety: A driving simulator study.

The connected environment provides surrounding traffic information to drivers via different driving aids that are expected to improve driving behavior and assist in avoiding safety-critical events. These driving aids include speed advisory, car-following assistance, lane-changing support, and advanced information about possible unseen hazards, among many others. While various studies have attempted to examine the effectiveness of different driving aids discretely, it is still vague how drivers perform when they are exposed to a connected environment with vehicle-to-vehicle and vehicle-to-infrastructure communication capabilities. As such, the objective of this study is to examine the effects of the connected environment on driving behavior and safety. To achieve this aim, an innovative driving simulator experiment was designed to mimic a connected environment using the CARRS-Q Advanced Driving Simulator. Two types of driving aids were disseminated in the connected environment: continuous and event-based information. Seventy-eight participants with diverse backgrounds drove the simulator in four driving conditions: baseline (without driving aids), perfect communication (uninterrupted supply of driving aids), communication delay (driving aids are delayed), and communication loss (intermittent loss of driving aids). Various key driving behavior indicators were analyzed and compared across various routine driving tasks such as car-following, lane-changing, interactions with traffic lights, and giving way to pedestrians at pedestrian crossings. Results suggest that drivers in the perfect communication scenario maintain a longer time-to-collision during car-following, a longer time-to-collision to pedestrian, a lower deceleration to avoid a crash during lane-changing, and a lower propensity of yellow light running. Overall, drivers in the connected environment are found to make informed (thus better) decisions towards safe driving.

Understanding the discretionary lane-changing behaviour in the connected environment.

Discretionary lane-changing (DLC) is one of the complex driving manoeuvres that requires surrounding traffic information for efficient and safe manoeuvring. The connected environment not only provides such information but also increases situational awareness, which is useful for DLC decision-making. However, the literature is devoid of any concrete evidence of such impact of the connected environment on DLC decision-making. As such, this paper analyses the effects of the connected environment on DLC behaviour. Seventy-eight participants from a diverse background performed DLCs in randomised driving conditions using the CARRS-Q advanced driving simulator. These driving conditions are: baseline (without driving messages), connected environment with perfect communication (fully functioning and uninterrupted supply of driving messages), and connected environment with communication delay (impaired communication). Various key driving behaviour indicators are analysed and compared using a linear mixed model. To analyse the effects of the connected environment on DLC decision-making, two Generalised Estimation Equation (GEE) models are developed for gap acceptance and DLC duration. In addition, a Weibull accelerated failure time hazard-based duration model is developed to investigate the impact of the connected environment on safety associated with DLC manoeuvres. We find that drivers in the connected environment have a larger spacing, larger lead and lag gaps, a longer DLC duration, and a lower acceleration noise compared to the baseline condition. The GEE model on gap acceptance reveals that drivers tend to select relatively bigger gap sizes when the connected environment offers them the subsequent gap information. Similarly, the GEE model for DLC duration suggests that the connected environment increases DLC durations by 2.22 s and 2.11 s in perfect communication and communication delay driving conditions, respectively. Finally, the hazard-based duration model provides insights into the probability of avoiding a lane-changing collision, and indicates that the probability of a lane-changing collision is less in the connected environment driving conditions than in the baseline scenario. Overall, the connected environment improves the DLC driving behaviour and enhances traffic safety.

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.

Does family background impact driving attitudes and risky behaviours? An investigation on Chinese young drivers.

The rapid pace of motorisation in China has been well documented, as has the large road trauma burden the Chinese citizens are facing as a result. China's unique political system represents an important consideration in helping reduce road trauma, yet political factors have not been previously investigated in this context. Recently, emerging issues on the road involving the adult children of politically powerful families have become a serious social problem in China, and have drawn widespread media and public attention. This study took a novel approach to examining factors associated with risky attitudes and risky road use in China by investigating the economic and political background status of a sample of young Chinese drivers. An online survey was conducted in May 2015 with a sample size of 476 Chinese young drivers from across the country, aged between 18 and 28, including 305 males and 171 females. The results suggest that for participants who reported having a familial political background, more risky driving behaviours were reported among those participants who reported more impact on their life from that political background; while for participants without political background, higher personal income was associated with more risky driving behaviours. The findings are discussed in light of China's political management system and potential education opportunities for young drivers.

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.

Impact of real-time traffic characteristics on freeway crash occurrence: systematic review and meta-analysis.

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge. This paper addresses this need by undertaking a systematic literature review to identify current knowledge, challenges, and opportunities, and then conducts a meta-analysis of existing studies to provide a summary impact of traffic characteristics on crash occurrence. Sensitivity analyses were conducted to assess quality, publication bias, and outlier bias of the various studies; and the time intervals used to measure traffic characteristics were also considered. As a result of this comprehensive and systematic review, issues in study designs, traffic and crash data, and model development and validation are discussed. Outcomes of this study are intended to provide researchers focused on real-time crash prediction with greater insight into the modeling of this important but extremely challenging safety issue.

Impact of traffic oscillations on freeway crash occurrences.

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion.