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This paper introduces a new model for electric vehicle mobility and energy consumption in urban traffic networks. The model couples the vehicle mobility described by a set of ODEs over a graph capturing the Origin-destination motion for urban networks,and the energy consumption associate to this mobility patterns. This model is illustrated in a simple pedagogic example showing its capabilities, such as keeping track of the vehicle state of charge, current energy and available storage.

Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data.

Intelligent transport systems are preparing to welcome connected and automated vehicles (CAVs), although it is uncertain which algorithms should be employed for the effective and efficient management of CAV systems. Even though remarkable improvements in telecommunication technologies, such as vehicle-to-everything (V2X), enable communication and computation sharing among different agents, e.g. vehicles and infrastructures, within existing approaches, a significant part of the computation burden is still typically assigned to central units. Distributed algorithms, on the other hand, could alleviate traffic units from most, if not all, of the high dimensional calculation duties, while improving security and remaining effective. In this paper, we propose a formation-control-inspired distributed algorithm to rearrange vehicles’ passing time periods through an intersection and a novel formulation of the underlying trajectory optimization problem so that vehicles need to exchange and process only a limited amount of information. We include early simulation results to demonstrate the effectiveness of our approach.

Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e. check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.

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