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.

This study provides a methodology to identify travellers’ transportation modes by tracking the mobile phone data, which aims to obtain the accurate mode split rate for providing decision support in urban traffic planning. First, the effective mobile phone singling data and GPS data are collected from the communication operators and a mobile phone app, respectively. Considering the differences in velocity and acceleration of different trip modes, a trip mode characteristic description model is built based on wave characteristics and moving average method. Compared with the wave characteristics, the moving average method shows a better accuracy of 90%. Then training samples are drawn by two data selection methods including probability proportional to size sampling and equal amount sampling. Furthermore, the classifier method for mode choice prediction is developed by support vector machines (SVMs) and back propagation neutral network. Finally, the results of the case study show that using a 30‐point moving average training data set can improve the prediction accuracy largely, and the SVM method gets a better accuracy of 82%. The potential of using the mobile phone data to build a new mode choice prediction method in the field of transportation is shown.

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.

The pollution and congestion caused by the shortage of parking spaces are threatening the sustainable development of cities. Smart parking platforms are one of the major tools to solve the problem by providing the efficient usage of parking resources. However, current platforms can only realize limited functions, and shared parking is far from being implemented on a large scale. Since off-street parking provides the majority of potential shared parking spaces, this paper takes periodic off-street parking lots as the starting point for opening the shared parking market. Based on data from the Ningbo Yongcheng parking platform, power spectral density (PSD) and the autocorrelation function (ACF) are used to identify periodic parking lots. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based method is applied to clustering the occupancy time series. Land use, user type, parking duration, and parking patterns are then analyzed to study shared parking supply characteristics. The results show that (1) 31.3% of off-street parking lots are periodic parking lots, and 90.3% of them have regular users exceeding 50%. (2) Periodic parking lots are classified into four types. Most parking lots show convex flat peak, double peak, or triple peak characteristics. (3) The shared parking spaces demonstrate spatial and temporal imbalances. But in a small area, even considering the concentration of land use and the peak period, there are still enough spaces available. The above research is of significance for the large-scale implementation of shared parking, which can promote the sustainable development of a city.