The impact of co-locating regulatory and directional signs on driver performance

The use of intelligent transport systems is proliferating across the Australian road network, particularly on major freeways. New technology allows a greater range of signs and messages to be displayed to drivers. While there has been a long history of human factors analyses of signage, no evaluation has been conducted on this novel, sometimes dynamic, signage or potential interactions when co-located. The purpose of this driving simulator study was to investigate drivers’ behavioural changes and comprehension resulting from the co-location of Lane Use Management Systems with static signs and (Enhanced) Variable Message Signs on Queensland motorways. A section of motorway was simulated, and nine scenarios were developed which presented a combination of signage cases across levels of driving task complexity. Two higher-risk road user groups were targeted for this research on an advanced driving simulator: older (65+ years, N=21) and younger (18-22 years, N=20) drivers. Changes in sign co-location and task complexity had small effect on driver comprehension of the signs and vehicle dynamics variables, including difference with the posted speed limit, headway, standard deviation of lane keeping and brake jerks. However, increasing the amount of information provided to drivers at a given location (by co-locating several signs) increased participants’ gaze duration on the signs. With co-location of signs and without added task complexity, a single gaze was over 2s for more than half of the population tested for both groups, and up to 6 seconds for some individuals.

On the use of Bluetooth MAC Scanners for live reporting of the transport network

Recently there has been significant interest of researchers and practitioners on the use of Bluetooth as a complementary transport data. However, literature is limited with the understanding of the Bluetooth MAC Scanner (BMS) based data acquisition process and the properties of the data being collected. This paper first provides an insight on the BMS data acquisition process. Thereafter, it discovers the interesting facts from analysis of the real BMS data from both motorway and arterial networks of Brisbane, Australia. The knowledge gained is helpful for researchers and practitioners to understand the BMS data being collected which is vital to the development of management and control algorithms using the data.

Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

The impact of incidents on macroscopic fundamental diagram

Macroscopic Fundamental Diagram (MFD) has been proved to exist in large urban road and freeway networks by theoretic method and real data in cities. However hysteresis and scatters have also been found existed both on motorway network and urban road. This paper investigates how the incident variables affect the scatter and shape of the MFD using both the simulated data and the real data collected from the Pacific Motorway M3 in Brisbane, Australia. Three key components of incident are investigated based on the simulated data: incident location, incident duration time and traffic demand. Results based on the simulated data indicate that MFD shape is a property not only of the network itself but also of the incident characteristics variables. MFDs for three types of real incidents (crash, hazard and breakdown) are explored separately. The results based on the empirical data are consistent with the simulated results. The hysteresis phenomenon occurs on both the upstream and the downstream of the incident location, but for opposite hysteresis loops. Gradient of the MFD for the upstream is more than that for the downstream on the incident site, when traffic demand is off peak.

Fuel consumption and gas emissions of an automatic transmission vehicle following simple eco-driving instructions on urban roads

Following eco-driving instructions can reduce fuel consumption between 5 to 20% on urban roads with manual cars. The majority of Australian cars have an automatic transmission gear-box. It is therefore of interest to verify whether current eco-driving instructions are e cient for such vehicles. In this pilot study, participants (N=13) drove an instrumented vehicle (Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants was compared before and after they received simple eco-driving instructions. Participants drove the same vehicle on the same urban route under similar tra c conditions. We found that participants drove at similar speeds during their baseline and eco-friendly drives, and reduced the level of their accelerations and decelerations during eco-driving. Fuel consumption decreased for the complete drive by 7%, but not on the motorway and inclined sections of the study. Gas emissions were estimated with the VT-micro model, and emissions of the studied pollutants (CO2, CO, NOX and HC) were reduced, but no di erence was observed for CO2 on the motorway and inclined sections. The di erence for the complete lap is 3% for CO2. We have found evidence showing that simple eco-driving instructions are e cient in the case of automatic transmission in an urban environment, but towards the lowest values of the spectrum of fuel consumption reduction from the di erent eco-driving studies.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

One night CPAP withdrawal impairs performance at a driving simulator task faster than sleep restriction to 5 hours with treatment in OSA patients

Introduction Sleep restriction and missing 1 night’s continuous positive air pressure (CPAP) treatment are scenarios faced by obstructive sleep apnoea (OSA) patients, who must then assess their own fitness to drive. This study aims to assess the impact of this on driving performance. Method 11 CPAP treated participants (50–75 yrs), drove an interactive car simulator under monotonous motorway conditions for 2 hours on 3 afternoons, following;(i)normal night’s sleep (average 8.2 h) with CPAP (ii) sleep restriction (5 h), with CPAP (iii)normal length of sleep, without CPAP. Driving incidents were noted if the car came out of the designated driving lane. EEG was recorded continually and KSS reported every 200 seconds. Results Driving incidents: Incidents were more prevalent following CPAP withdrawal during hour 1, demonstrating a significant condition time interaction [F(6,60) = 3.40, p = 0.006]. KSS: At the start of driving participants felt sleepiest following CPAP withdrawal, by the end of the task KSS levels were similar following CPAP withdrawal and sleep restriction, demonstrating a significant condition, time interaction [F(3.94,39.41) = 3.39, p = 0.018]. EEG: There was a non significant trend for combined alpha and theta activity to be highest throughout the drive following CPAP withdrawal. Discussion CPAP withdrawal impairs driving simulator performance sooner than restricting sleep to 5 h with CPAP. Participants had insight into this increased sleepiness reflected by the higher KSS reported following CPAP withdrawal. In the practical terms of driving any one incident could be fatal. The earlier impairment reported here demonstrates the potential danger of missing CPAP treatment and highlights the benefit of CPAP treatment even when sleep time is short.

Is the two hour recommended maximum driving time appropriate for both healthy older and treated obstructive sleep apnoea drivers?

Objectives The UK Department for Transport recommends taking a break from driving every 2 h. This study investigated: (i) if a 2 h drive time on a monotonous road is appropriate for OSA patients treated with CPAP, compared with healthy age matched controls, (ii) the impact of a night’s sleep restriction (with CPAP) and (iii) what happens if these patients miss one nights’ CPAP treatment. Methods About 19 healthy men aged 52–74 y (m = 66.2 y) and 19 OSA participants aged 50–75 y (m = 64.4 y) drove an interactive car simulator under monotonous motorway conditions for 2 h on two afternoons, in a counterbalanced design; (1) following a normal night’s sleep (8 h). (2) following a restricted night’s sleep (5 h), with normal CPAP use (3) following a night without CPAP treatment. (n = 11) Lane drifting incidents, indicative of falling asleep, were recorded for up to 2 h depending on competence to continue driving. Results Normal sleep: Controls drove for an average of 95.9 min (s.d. 37 min) and treated OSA drivers for 89.6 min (s.d. 29 min) without incident. 63.2% of controls and 42.1% of OSA drivers successfully completed the drive without an incident. Sleep restriction: 47.4% of controls and 26.3% OSA drivers finished without incident. Overall: controls drove for an average of 89.5 min (s.d. 39 min) and treated OSA drivers 65 min (s.d. 42 min) without incident. The effect of condition was significant [F(1.36) = 9.237, P < 0.05, eta2 = 0.204]. Stopping CPAP: 18.2% of drivers successfully completed the drive. Overall, participants drove for an average of 50.1 min (s.d. 38 min) without incident. The effect of condition was significant [F(2) = 8.8, P < 0.05, eta2 = 0.468]. Conclusion 52.6% of all drivers were able to complete a 2 hour drive under monotonous conditions after a full night’s sleep. Sleep restriction significantly affected both control and OSA drivers. We find evidence that treated OSA drivers are more impaired by sleep restriction than healthy control, as they were less able to sustain safely the 2 h drive without incidents. OSA drivers should be aware that non-compliance with CPAP can significantly impair driving performance. It may be appropriate to recommend older drivers take a break from driving every 90 min especially when undertaking a monotonous drive, as was the case here.

Strategies for rapid congestion recovery using ramp metering

This research investigated strategies for motorway congestion management from a different angle: that is, how to quickly recover motorway from congestion at the end of peak hours, given congestion cannot be eliminated due to excessive demand during the long peak hours nowadays. The project developed a zone recovery strategy using ramp metering for rapid congestion recovery, and a serious of traffic simulation investigations were included to evaluate the developed strategy. The results, from both microscopic and macroscopic simulation, demonstrated the effectiveness of the zone recovery strategy.

Sleep-related eye symptoms and their potential for identifying driver sleepiness

The majority of individuals appear to have insight into their own sleepiness, but there is some evidence that this does not hold true for all, for example treated patients with obstructive sleep apnoea. Identification of sleep-related symptoms may help drivers determine their sleepiness, eye symptoms in particular show promise. Sixteen participants completed four motorway drives on two separate occasions. Drives were completed during daytime and night-time in both a driving simulator and on the real road. Ten eye symptoms were rated at the end of each drive, and compared with driving performance and subjective and objective sleep metrics recorded during driving. ‘Eye strain’, ‘difficulty focusing’, ‘heavy eyelids’ and ‘difficulty keeping the eyes open’ were identified as the four key sleep-related eye symptoms. Drives resulting in these eye symptoms were more likely to have high subjective sleepiness and more line crossings than drives where similar eye discomfort was not reported. Furthermore, drivers having unintentional line crossings were likely to have ‘heavy eyelids’ and ‘difficulty keeping the eyes open’. Results suggest that drivers struggling to identify sleepiness could be assisted with the advice ‘stop driving if you feel sleepy and/or have heavy eyelids or difficulty keeping your eyes open’.

The impacts of variable speed limit on speed variation and headway distribution

This thesis investigates the impacts of variable speed limit on motorway speed variation and headway distribution. Initiative techniques of traffic flow categorisation study contribute in analysing the effects of variable speed limit on various traffic states. The project focuses on the speed harmonisation impacts within and across lanes as well as the uniformity of headway spread in the application of variable speed limit.

A ramp metering strategy for rapid congestion recovery

Recurrent congestion caused by high commuter traffic is an irritation to motorway users. Ramp metering (RM) is the most effective motorway control means (M Papageorgiou & Kotsialos, 2002) for significantly reducing motorway congestion. However, given field constraints (e.g. limited ramp space and maximum ramp waiting time), RM cannot eliminate recurrent congestion during the increased long peak hours. This paper, therefore, focuses on rapid congestion recovery to further improve RM systems: that is, to quickly clear congestion in recovery periods. The feasibility of using RM for recovery is analyzed, and a zone recovery strategy (ZRS) for RM is proposed. Note that this study assumes no incident and demand management involved, i.e. no re-routing behavior and strategy considered. This strategy is modeled, calibrated and tested in the northbound model of the Pacific Motorway, Brisbane, Australia in a micro-simulation environment for recurrent congestion scenario, and evaluation results have justified its effectiveness.

Proactive safety assessment in roadwork zones : a synthesis of surrogate measures of safety

Work zone safety studies have traditionally relied on historical crash records—an approach which is reactive in nature as it requires crashes to accumulate first before taking any preventive actions. However, detailed and accurate data on work zone crashes are often not available, as is the case for Australian road work zones. The lack of reliable safety records and the reactive nature of the crash-based safety analysis approach motivated this research to seek alternative and proactive measures of safety. Various surrogate measures of safety have been developed in the traffic safety literature including time to collision, time to accident, gap time, post encroachment time, required deceleration rate, proportion of stopping distances, lateral distance to departure, and time to departure. These measures express how close road-user(s) are from a potential crash by analysing their movement trajectories. A review of this fast-growing literature is presented in this paper from the viewpoint of applying the measures to untangle work zone safety issues. The review revealed that the use of the surrogate measures is very limited for analysing work zone safety, although numerous studies have used these measures for analysing safety in other parts of the road network, such as intersections and motorway ramps. There exist great opportunities for adopting this proactive safety assessment approach to transform work zone safety for both roadworkers and motorists.

Reducing rear-end crashes with cooperative systems

This paper presents an evaluation of the effectiveness of a cooperative Intelligent Transport System (C-ITS) to reduce rear-end crashes. Two complementary simulation techniques are used to demonstrate the benefits of the C-ITS. A traffic (VEINS) and sensor (SiVIC) simulations use realistic data related to traffic/road in Brisbane’s Pacific Motorway, driver’s reaction time and injury severity to evaluate benefits. The results of our simulations show that C-ITS could reduce rear-end crash risk by providing several seconds of additional warning to drivers.