Appertaining parking lots of public buildings provide a large proportion of parking supply in cities. However, these parking lots mainly serve the parking demands of public buildings, leading to a low utilization ratio of parking spaces. It is therefore required to implement a shared parking strategy for these parking lots. In this study, a parking space allocation method (PSAM) at the network level is proposed to allocate the parking demand to a parking lot and then the parking space. The users are divided into M-users (users of the buildings) and P-users (public users). The shared parking strategy is analyzed from the aspects of open window, parking fee, and ratio of reservation spaces. The users are allocated to a parking lot by a multinomial logit(MNL) model. Specifically, it is determined whether they can enter parking lot and which space they are allocated according to the specific rules. After all the users are allocated with a parking space, the rejection number of M-users, occupancy rate, and profits of each parking lot are collected and a NSGA-II (non-dominated sorting genetic algorithm II) algorithm is designed to determine the optimal strategy for each parking lot according to the above. Compared with the results of all-time all-space shared parking strategy, our method shows better performance in balancing the interests of all appertaining parking lots and protecting the interests of M-users while obtaining considerable profits for the parking lots.

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Road accidents impose serious problems on society. Possible collisions between vehicles and pedestrians must be detected before they occur so that a timely warning may be issued. By using the vision‐based approach, this study presents an effective and efficient algorithm to estimate the vehicle–pedestrian collision probability at intersections. The real‐time trajectories and movement parameters (position, speed, acceleration or direction) of vehicles and pedestrians are obtained based on state‐of‐the‐art detection and tracking algorithm which include background subtraction method, faster regions with convolutional neural networks and optical flow method. To find the appropriate time to identify the latent collision risk for calculating the collision probability, this study defines the critical time based on different collision patterns of perception‐reaction failure and evasive action failure. In addition, based on discrete acceleration and discrete angle, the authors get different extended trajectories which can include most situation when the conflict happened. Trajectories generation probability are given by the discrete choice probability model based on the Logit model to get the accurate collision probability. Real‐world video data is implemented to demonstrate the approach. This proposed collision prediction method can provide some important results for designing the intelligent pedestrian signal timing schemes at intersections.

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Integrated transportation is one of the most important methods to encourage the modal shift from car to public transportation (PT). However, as most cities have an existing multimodal network, it is difficult to expand the current networks by building more PT routes. Thus, integrating different modes through the optimization of hubs is a cost-efficient way to promote sustainable mobility. This paper develops a bilevel multimodal network design problem based on the collaborative optimization of urban transportation hubs. The upper-level problem is formulated as a mixed-integer nonlinear program to achieve a modal shift from congested subnetworks to underutilized subnetworks to realize a balanced use of the entire network. The decision variables are classified into location-based (hub locations) and route-based (route layouts and frequency setting) ones. The lower-level problem is a generalized modal split/traffic assignment problem (GMS/TAP), which captures the mode choices of all modes in the path set. The GMS/TAP is formulated as a nonlinear optimization problem (NLP) and is solved using a hybrid method of the successive average (MSA) algorithm. A hybrid genetic search with advanced diversity control (HGSADC) is developed to solve the bilevel model, where the exploration of the search space is expanded using the biased fitness function and diversification mechanism. The solution properties of the hybrid MSA and HGSADC are demonstrated in two modified nine-node networks. The model performance is illustrated in a real-size network in Jianye district, Nanjing. 9.2% decrease of travel time, 25.7% increase of service level, and a significant modal shift from car to PT are obtained.