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This paper models road traffic demand of container trucks in a Chinese container port. The model is helpful for road planning and traffic management in the port. First, we analyze the attributes of road traffic in the port, to distinguish the differences between port area traffic and surrounding urban area traffic. It is found that port area traffic is generated from vessel operations and fluctuates corresponding to two major factors: operation time schedule and the truck arrival pattern within an operation time window. Since the first factor is available from terminal operators, the truck arrival pattern is the key for modeling port area traffic demand. Second, we conduct a comprehensive survey to collect truck traffic and vessel operation data, which is used to explore the probability distribution of truck arrivals during the consolidation periods. Third, we develop a traffic demand model based on the probability distribution with unknown parameters, and modify this model by taking the effects of some external factors into account. At last, we estimate the parameters with the collected data, and the result indicates that this traffic demand model has a high quality of estimation especially for peak traffic times. DOI: 10.1061/(ASCE)TE.1943-5436.0000152 CE Database subject headings: Marine terminals; Freight transportation; Containers. Author keywords: Marine terminal; Container consolidation; Truck arrival; Distribution.

In this paper, a bilevel programming model for the bus frequency design is presented, which determines the optimal bus frequencies aiming to minimize the total travel time of passengers subject to the constraint on the overall fleet size of each company by accounting for the route choice behaviors of the users. The objective of the lower level is to assign transit trips to bus route network based on optimal strategy. In the upper level, bus frequencies of routes are optimized as a result of passenger assignment. An iterative approach, which consists of a genetic algorithm and a label-marking method, is used to solve the bilevel model. Finally, the model and the algorithms are illustrated with two test examples. The results show that the optimization can improve the local service level of one company, and the proper integration of several companies probably improves the efficiency of resources and the service level of the whole transit system. DOI: 10.1061/(ASCE)TE.1943-5436.0000119 CE Database subject headings: Algorithms; Buses; Trip frequencies. Author keywords: Bilevel model; Frequency; Assignment; GA.

This paper addresses a problem in evaluating the impacts of internet-app-based dockless public bicycles and public transport (i-PB&PT) on young people's mode choices considering their choice intentions. A method is proposed, based on the theory of planned behavior and structural equation modeling, to identify changes in the modal split and in travelers' willingness to buy and use private cars after having the intention to use the i-PB&PT mode. Guangzhou City was used as the study case, and an stated preference (SP) surveys was conducted. (1) The density of i-PBs was increased and, accordingly, the reduced cost of i-PB use strengthened travelers' intentions to choose i-PB&PT. For example, when the bicycle search time decreased from 4 to 1 min, the i-PB&PT modal split increased from 38.21% to 41.28%, whereas, if the transfer time between PT and i-PB use decreased from 3.5 to 2 min, the modal split of the combined mode increased from 40.72% to 49.59%. (2) Car ownership decreased from 1,190,465 (for the case without bicycles) to 650,556 as the travelers' intention (endogenous latent variable) to choose the combined mode became stronger. In particular, the values of the three exogenous latent variables (E––emotional attitudes, O––satisfaction, and S––subjective norms) increased (E = 2 to E = 5; OS = 1 to O = 4; S = 2 to S = 4). Therefore, the large-scale implementation and widespread use of the i-PB&PT model should significantly decrease car travel and transport emissions. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze shopping center (SC) location distribution for a small city with respect to its environmental impact and retail supply quality. In order to evaluate the retail supply quality for each household and the environment impact of each shopping center, a geographic information system-based microanalysis was performed. Furthermore, the analytical hierarchy process method was employed to determine optimal SC locations in the city, taking into consideration both the quality of life of the consumer and the impact of the SCs on the environment. CE Database keywords: Shopping centers; Location; Environmental impacts.

Dynamic vehicle dispatching at the transfer station can improve the transit service quality by optimizing the transfer coordination of routes. In this paper, a dynamic vehicle dispatching model is proposed that aims to minimize the total waiting time of passengers at the transfer station and the downstream stops. A prediction model based on support vector machines (SVM) is also developed to forecast the arrival time of the next vehicles at the transfer station. To reduce the disconnected cases between the transfer routes, an SVM-based model is introduced to forecast the elastic time for the estimated arrival time at the transfer station. According to the estimated arrival time and elastic time, vehicles are dispatched in a dynamic way to reduce the total waiting time of passengers. The dynamic vehicle dispatching approach at the transfer station is examined with the data of three transfer routes in the city of Dalian, China. Results show that the approach proposed in this paper can reduce the total waiting time of passengers at the transfer station and the downstream stops. [ABSTRACT FROM AUTHOR]