数字化转型时代下新能源汽车智能化发展研究

本文基于数字化转型时代,围绕新能源汽车智能化发展,对智能网联、智能驾驶以及智能共享三个方面进行研究。首先对于已有文献进行梳理总结,从而分析新能源汽车智能化发展现状。其次指出数字化转型时代下新能源汽车存在的问题,并针对新能源汽车智能化发展存在的问题提出相关建议。     

基于云架构的线网监控系统方案研究

研究云平台应用架构、提供业务种类、承载业务划分,对轨道交通云平台建设方案给出可行的建设思路.结合线网控制中心云建设,提出业务需立足于网络层面,对各线路与各专业系统及各个建设与资源运作项目的管理业务进行集中管理和统一协调,把各业务统一到网络层面上进行分析应用,替代传统上各系统间的接口关系,更好的为网络化建设提供支撑;同时...     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.     

Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat, 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data.     

A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services

Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.     

A framework to evaluate policy options for supporting electric vehicles in urban freight transport

Electric vehicles (EVs) are considered as a feasible alternative to traditional vehicles. Few studies have addressed the impacts of policies supporting EVs in urban freight transport. To cast light on this topic, we established a framework combining an optimization model with economic analysis to determine the optimal behavior of an individual delivery service provider company and social impacts (e.g., externalities and welfare) in response to policies designed to support EVs, such as purchase subsidy, limited access (zone fee) to congestion/low-emission zones with exemptions for EVs, and vehicle taxes with exemptions for EVs. Numerical experiments showed that the zone fee can increase the company’s total logistics costs but improve the social welfare. It greatly reduced the external cost inside the congestion/low-emission zone with a high population, dense pollution, and heavy traffic. The vehicle taxes and subsidy were found to have the same influence on the company and society, although they have different effects with low tax/subsidy rates because their different effects on vehicle routing plans. Finally, we performed a sensitivity analysis. Local factors at the company and city levels (e.g., types of vehicle and transport network) are also important to designing efficient policies for urban logistics that support EVs.     

From hierarchy to networking: the evolution of the “twenty-first-century Maritime Silk Road” container shipping system

ABSTRACT

Container shipping gives a rise of international trade since the 1960s. Based on navigation data start from the mid-1990s to 2016, this paper empirically analyses the spatial pattern of China’s international maritime linkages along the “twenty-first-century Maritime Silk Road”. We interpret such evolutionary dynamics in terms of growth, hierarchical diffusion and networking phases. Networking is a new stage of the evolution of the port system, which is approached based on the graph theory, complex network methods and geomatics, the paper discusses the networking’s basic characteristics: multi-hub spatial agglomeration, the connection of the network develops across space, functional differentiation and a division of labour appear among ports. Our results show that, while the scope of China’s maritime linkages had expanded overtime, more foreign ports become connected to the “Maritime Silk Road”. In addition, the external linkages of domestic ports tend to be dispersed, reflecting upon the decline of Pearl River Delta ports and the rise of Yangtze River Delta ports, with mixed evidence for the Bohai Rim region. Lastly, the analysis underlines the emergence of a polycentric shipping system, from the Hong Kong dominance to the more diversified Shanghai/Ningbo/Shenzhen configuration. Academic and managerial implications are included.     

Tweeting Transit: An examination of social media strategies for transport information management during a large event

Social media platforms are seeing increasing adoption by public transport agencies, as they provide a cost-effective, reliable, and timely mechanism for sharing information with passengers and other travellers. In this paper, we use a case study of the @GamesTravel2014 Twitter account to evaluate how this social media platform was used over the course of the 2014 Commonwealth Games in Glasgow, Scotland to provide and share transport-related information and respond to information requests. The case study provides an exemplar for the public co-ordination of information from multiple partners in a complex environment during a time of transport disruption. We evaluate both the structure and intent of the @GamesTravel2014 social media strategy via interviews with involved parties and an analysis of Tweets related to the account. Findings indicate the potential for future applications of social media by transport operators and authorities in producing a more effective network of communication with passengers.     

重庆城市轨道交通调度指挥系统的网络化模式

根据重庆城市轨道交通的发展情况,从城市轨道交通运营调度指挥的角度,介绍了调度指挥系统的职能、网络化模式和发展,并对重庆城市轨道交通两路口调度指挥中心的建设提出设想与建议.