Evaluating the Accuracy of Bluetooth-Based Travel Time on Arterial Roads: A Case Study of Perth, Western Australia

Bluetooth (BT) time-stamped media access control (MAC) address data have been used for traffic studies worldwide. Although Bluetooth (BT) technology has been widely recognised as an effective, low-cost traffic data source in freeway traffic contexts, it is still unclear whether BT technology can provide accurate travel time (TT) information in complex urban traffic environments. Therefore, this empirical study aims to systematically evaluate the accuracy of BT travel time estimates in urban arterial contexts. There are two major hurdles to deriving accurate TT information for arterial roads: the multiple detection problem and noise in BT estimates. To date, they have not been fully investigated, nor have well-accepted solutions been found. Using approximately two million records of BT time-stamped MAC address data from twenty weekdays, this study uses five different BT TT-matching methods to investigate and quantify the impact of multiple detection problems and the noise in BT TT estimates on the accuracy of average BT travel times. Our work shows that accurate Bluetooth-based travel time information on signalised arterial roads can be derived if an appropriate matching method can be selected to smooth out the remaining noise in the filtered travel time estimates. Overall, average-to-average and last-to-last matching methods are best for long (>1 km) and short (≤1 km) signalised arterial road segments, respectively. Furthermore, our results show that the differences between BT and ground truth average TTs or speeds are systematic, and adding a calibration is a pragmatic method to correct inaccurate BT average TTs or speeds. The results of this research can help researchers and road operators to better understand BT technology for TT analysis and consequently to optimise the deployment location and configuration of BT MAC address scanners

Multimodal Traffic Speed Monitoring: A Real-Time System Based on Passive Wi-Fi and Bluetooth Sensing Technology

SED-000080This manuscript was originally printed in the IEEE Transactions on Intelligent Transportation Systems, Volume 9, Issue 14.Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors\u2019 detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy C-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network

Improving Traffic Safety and Efficiency by Adaptive Signal Control Systems Based on Deep Reinforcement Learning

As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist\u27s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety

Improving Queensland speed zoning practices

Queensland speed limits are assessed against the guidelines outlined within Part 4 of the Manual of Uniform Traffic Control Devices (MUTCD Part 4), which is maintained by the Queensland Department of Transport and Main Roads. This project was undertaken in order to develop recommendations for improvements in future revisions of MUTCD Part 4 that meet the needs of local government and industry users. The current framework outlined within MUTCD Part 4 can be difficult for practitioners to follow and often adds unnecessary cost and complexity to speed zoning processes. Results between different users may be inconsistent as a result. It is also structured towards application on State roads, which means that it does not consistently align with local government needs regarding transport planning and traffic operations. It is believed that amendments to particular elements of the guidelines will increase practicality in application and ensure consistent speed zoning in Queensland. Local and international guidelines for speed zoning were reviewed to understand the processes undertaken by other road authorities. The possibilities of using speed measuring technology and risk assessment tools to analyse speed limits were also considered. Interviews were conducted to identify stakeholder issues with MUTCD Part 4, and to assist in making informed recommendations for future revisions. Additionally, case studies were conducted using different speed zoning processes on a sample of roads to identify the strengths and weaknesses of processes used by other state and international road authorities. These results were compared to those obtained using MUTCD Part 4. Project tasks highlighted numerous aspects of MUTCD Part 4 that could be improved and provided a basis for recommendations to be considered in future revisions of the guidelines. Suggested recommendations include amendments to road function classification, criteria-based speed limits for all speed limits, flowchart mapping of processes for clarity, inclusion of design guidance to effect speed reductions and updates to the online assessment tool, QLIMITS. If adopted by the Department of Transport and Main Roads, future amendments to MUTCD Part 4 may result in more consistency in speed zoning practise and provide a document that will be practical for transport planning purposes. The suggested recommendations may also contribute to improving community understanding and acceptance of Speed Zoning procedures. Further work after completion of this project involves approaching The Department of Transport and Main Roads to discuss the project and suggested recommendations for consideration in future amendments to MUTCD Part 4