Demand variations throughout the day and area popularity differences across the city result in spatiotemporal changes in traffic flow. One of the well-known phenomena arising from these changes is tidal traffic, characterized by an imbalance between inbound and outbound traffic on a given road. It reflects the fluctuation in the alignment between transportation system supply and demand. Lane reversal control has been a common supply-side measure for dealing with this urban traffic “sickness” by adapting road capacity allocation to the demand imbalance between two directions of a road. This study investigates the dynamic network capacity allocation control problem in the era of connected and autonomous vehicles (CAVs), which integrates dynamic traffic signal splits and lane reversal controls. Considering the high dimensionality and non-linearity of urban transportation systems, we apply the sparse identification of nonlinear dynamics (SINDy) technique to construct a sparse yet sufficiently accurate surrogate model. This model estimates the forthcoming network traffic state based on the current state and implemented control decisions. The surrogate model is integrated into a model predictive control (MPC) method, forming a SINDy-MPC framework to assist in optimal decision-making in real time. The experiments show that the system identified by SINDy exhibits stability in the presence of Gaussian noise disturbances. The proposed dynamic network allocation control scheme can effectively reduce traffic imbalance, improve traffic efficiency, and enhance traffic resilience against cyberattacks.

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews.

Contemporary urban transportation systems frequently depend on a variety of modes to provide residents with travel services. Understanding a multimodal transportation system is pivotal for devising well-informed planning; however, it is also inherently challenging for traffic analysts and planners. This challenge stems from the necessity of evaluating and contrasting the quality of transportation services across multiple modes. Existing methods are constrained in offering comprehensive insights into the system, primarily due to the inadequacy of multimodal traffic data necessary for fair comparisons and their inability to equip analysts and planners with the means for exploration and reasoned analysis within the urban spatial context. To this end, we first acquire sufficient multimodal trips leveraging well-established navigation platforms that can estimate the routes with the least travel time given an origin and a destination (an OD pair). We also propose TraDyssey, a visual analytics system that enables analysts and planners to evaluate and compare multiple modes by exploring acquired massive multimodal trips. TraDyssey follows a streamlined query-and-explore workflow supported by user-friendly and effective interactive visualizations. Specifically, a revisited difference-aware parallel coordinate plot (PCP) is designed for overall mode comparisons based on multimodal trips. Trip groups can be flexibly queried on the PCP based on differential features across modes. The queried trips are then organized and presented on a geographic map by OD pairs, forming a group-OD-trip hierarchy of visual exploration. Domain experts gained valuable insights into transportation planning through real-world case studies using TraDyssey.