Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166267/1/itr2bf00710.pd

Forder Application

Dissertação de Mestrado em Engenharia InformáticaIn Portugal eating out is a part of the lifestyle. People meet in coffee shops and restaurants, creating business opportunities for the owners of the places. In the summer season there are many bars that open their terrace service. Like many business, there are some ‘quiet times’ during the day – moments, when the place doesn’t receive so many clients. This project proposes an idea on how to maintain the efficiency of the outdoor service with possibly lower costs for the company. The application presented in the given project enables clients to make their requests directly from the table using a cellphone. In the next step the employee receives a notification with the request and he can prepare and deliver the order. Combining Proximity Communication Technologies and a web and mobile application, the communication between a client and an employee may turn out to be fast and comfortable. This solution can have an impact on the number of employees during a calmer time. It is also expected that the client will be able to receive his order in the faster way, through the implemented innovation

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

Incidental Sensor Networks for Human Mobility Detection

Transportation systems need to get better by moving ever more people while consuming ever fewer resources. To build better transportation systems, planners need an accurate understanding of how people exercise mobility and tools to apply that understanding to the transportation system. Such an understanding can come through the development of existing sources of implicit and explicit mobility data and tools suitable for planners to apply the results. Transportation organizations may struggle to produce the necessary tools internally, leaving external bodies, both public and private, to pursue development. In this research, three frameworks were developed that employ data already being collected to facilitate analysis of human mobility and improve the utilization of that analysis in its application to transportation systems. First, two new metrics as potential objectives for finding solutions to a type of Urban Transit Routing Problem (UTRP) are proposed and applied. The metrics assess the social experience of transit users and can be used to produce transit routes that may improve a rider’s transit experience. In the presented case study, the improved routes increased the social metrics by 242% and 119% compared to current baseline routes. Next, the UTRP construct is again adapted to produce solutions that allow transit planners to balance the need to reduce the susceptibility of disease transmission in their transit vehicles while maintaining transit network utility for potential riders. In the presented case study, a Pareto front is produced of solutions from which a transit planner could choose what best suits their community’s needs. Both the UTRP-type frameworks use a novel source of mobility data to simulate the solutions’ impacts in a real-world environment. Finally, further exploring new uses of mobility data, an anomaly detection framework that leverages redundancies in sampling populations that will arise as additional sources of data are identified is developed. The anomaly detection framework provides increased quality assurance to planners as new sources of data are developed. In the presented case study, a previously unacknowledged anomaly in traffic data is successfully identified. The three frameworks demonstrate the potential of advancing the use of additional data in transportation planning. Future work requires additional resources to support data-driven transportation planning and adapting proven practices from elsewhere to the specific US transportation needs