Congestion reduction via personalized incentives
Cite this dataset
Ghafelebashi, Ali; Razaviyayn, Meisam; Dessouky, Maged (2022). Congestion reduction via personalized incentives [Dataset]. Dryad. https://doi.org/10.5061/dryad.ncjsxkst8
The purpose of this research is to develop real-time algorithms to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers. The incentives and alternative routes should be chosen smartly in order to maximize the probability of acceptance by drivers and to avoid the creation of new congestion in other areas of the network. To this end, we propose to exploit the wide-accessibility of smart communication devices and develop a real-time look-ahead incentive offering mechanism using individuals’ routing and aggregate traffic information. The proposed approach relies on historical data and state-of-the-art traffic prediction methodologies to continually predict congestion and traffic flow of the network. Using this prediction and based on individual preferences, the central controller offers personalized incentives to drivers with the goal of reducing the probability of congestion. The decisions about incentives are made via solving a series of carefully designed large-scale stochastic optimization problems. The performance of the proposed algorithms are evaluated using data from the Los Angeles area. Finally, we evaluate the performance of our method using data from the Los Angeles area. The Los Angeles region is ideally suited for being the validation area since there are a number of dedicated carpool lanes in the region and furthermore, there are portions of the freeway network where congestion pricing is employed with the added feature that ridesharing vehicles can travel on these lanes free of charge (e.g., I-110). Additionally, researchers at USC have developed the Archived Data Management System (ADMS) that collects, archives, and integrates a variety of transportation datasets from Los Angeles, Orange, San Bernardino, Riverside, and Ventura Counties.
The data is from the traffic data of the Los Angeles area. Researchers at USC have developed the Archived Data Management System (ADMS) that collects, archives, and integrates a variety of transportation datasets from Los Angeles, Orange, San Bernardino, Riverside, and Ventura Counties. ADMS includes access to real-time traffic datasets from i) 9500 highway and arterial loop detectors providing data approximately every 1 minute, and ii) 2500 bus and train GPS location (AVL) data operating throughout Los Angeles County.
First, we export our data from the ADMS systems. Next, we preprocess the data based on our criteria such as the type of sensors, the time interval of data, and region. Also, we extract the graph of the network on Networkx. Finally, we use the data to do OD estimation and solve our optimization model.
More details are provided in the readme.txt file.
Details are provided in the readme.txt file. We can share a sample of our run upon a reasonable request.