![]() Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. A further benefit is the agnostic nature of the social media platform’s approach. Overall, SocialMedia2Traffic further improves the usability of the tested feature space for traffic prediction. The lack of a universal definition on a global scale to classify road segments by speed bins into different traffic congestion classes has been identified to be a major limitation of the transferability of the framework. ![]() Different data processing steps including ways to aggregate data points, different proxies and machine learning approaches were compared. The overall precision of the forecast for highly traffic-congested regions was approximately 81%. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data. The presented framework and workflow are termed as SocialMedia2Traffic. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. Unfortunately, open data are rarely available for this purpose. Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average). Moreover, the learned model parameters are found to be transferable from one region to another. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and São Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1–40 km). The model is tested on sparse mobility traces from Twitter. ![]() The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. However, these data suffer from sparsity, an issue that has largely been overlooked. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. Knowing how much people travel is essential for transport planning. The results show high accuracy under a tight computational budget. We validated the method with synthetic data and for a real-world case study in Tartu city, Estonia. Moreover, with receiving the field measurements as a stream and the efficient time complexity of the algorithm in each time frame, the method successfully presents a solution for high-dimensional real-time and online applications. The model does not depend on reliable prior demand information. ![]() The proposed sequential calibration model presents a drastic computational advantage over available methods by splitting the computations into short time frames and under the assumption that the demand in each time frame only depends on current and previous time frames. In addition, the algorithm's convergence is accelerated by applying the fixed-point method to road-segment travel times. At the end of each time frame, the microsimulation state of the network is transferred to the next time frame to ensure the temporal dependency and continuity of the estimations during the time. For every time frame, the probabilistic parameters of the route choice model are obtained through several rounds of feedback loop between the OD optimization problem (at the upper level) and parallel samplings and simulations for DTA (at the lower level). The upper-level optimization problem is presented as a bounded variable quadratic programming in each time frame, making it computationally tractable. The proposed method builds upon the standard bi-level optimization formulation. The calibration approach is based on sequential optimization demand estimation for short time frames and uses a stream of traffic count data from IoT sensors on selected roads. This paper presents a simulation-based optimization framework for city-scale real-time estimation and calibration of dynamic demand models by focusing on disaggregated microsimulation in congested networks. ![]()
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