Consumptive mobility, such as shopping and entertainment activities, which are discretionary and flexible travel behavior, are influenced by the socioeconomic and spatial attributes of heterogeneous individual travelers. But limited by the difficulty of collecting large-scale granular individual traveler profile attribute datasets, achieving a comprehensive understanding of the intricate determinants consumptive mobility patterns remains challenging, even when relying on a complete sample. In this study, we organize our analysis by representative shopping mall, and leverage precious mobile internet usage data, that containing the preferred websites and applications (apps) visited by mobile phone carrier. And then, they are integrated into portraits of traffic analysis zones (TAZs) that travel to shopping malls. To examine the determinants of travel volumes to shopping malls, we pre-select multiple key elements and employ a partial least squares (PLS) regression model. In our empirical studies, we test our framework in the real Suzhou, China, using a mobile phone dataset that provides comprehensive spatiotemporal coverage. This dataset includes the positioning data and socioeconomic data estimated based on internet usage preferences of the same users. Our findings reveal that distance, entertainment, dining, and shopping have a significantly higher impact on travel volumes compared to social activities, learning, travel, age, and gender.

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Metro transit is the core of urban transportation, and the mobility analysis of metro ridership can contribute to enhance the overall service level of the metro transit. Researchers studying metro ridership are focused on the spatiotemporal distribution characteristics of the ridership in the underground system of metro station by metro smart card data. However, limited by lack of travel mobility chain of ridership integrity, their activity patterns cannot be used to identify the heterogeneity of metro ridership's origin and transfer travel mode. In our research, we applied full spatiotemporal coverage of mobile phone data to identify the complete travel mobility of metro ridership in the perspective of ground and underground transit. First, the mobility of the boarding and alighting stations was extracted and the order of the transfer station was then extracted. Second, relying on the ridership flow identification method, the aboveground origin and destination of the ridership outside the metro system were extracted, and their transferred traffic mode was identified. The empirical results have shown that our proposed framework can accurately analyze the mobility patterns of metro ridership in an aboveground area and underground station.

In recent years, while the logistics industry is promoting economic growth, its energy consumption is also increasing year by year. The environmental pollution caused by logistics enterprises in the transportation process cannot be ignored. To facilitate the energy-saving and low-carbon development of logistics enterprises, the "top-down" method is used to calculate the mobile carbon emissions of land transportation and air transportation modes. This paper selects energy consumption, capital stock and labor as input variables, and business income and carbon emissions as output variables to develop a Super-efficiency Slack-based Measurement（Super-SBM） model, which is used to analyze the efficiency of carbon emission. In addition, the Logarithmic Mean Divisia Index（LMDI） is used to decompose the driving factors of carbon emissions into four categories. Their impact on carbon emissions of logistics enterprises is analyzed. This paper takes Shunfeng Express Enterprise as an example, and evaluates its efficiency of carbon emission according to its operating data from 2016 to 2021. The results show that the comprehensive technical efficiency value continues to grow and remain above 1.0 after 2019. The pure technical efficiency value fluctuates in the range of 1.1.The scale efficiency values are all below 1.0 but continue to grow. The results show that Shunfeng can carry out technological innovation on the original scale, and strive to obtain the highest output with the least input. Thus their resources can be reasonably allocated. Moreover, the level of economic development and population size both are the influencing factors that related to carbon emissions. The energy efficiency largely inhibits the increase of carbon emissions. The influence of energy consumption structure in promoting carbon emissions is relatively limited. The experimental results verified the effectiveness of the proposed measurement method. The Super-SBM and LMDI models can effectively evaluate the efficiency of carbon emission of logistics enterprises and analyze the influence of driving factors on carbon emission.

将思政理念、元素融入到《运输系统规划与设计》课程教学中,重构课程教学路径,具体包含:确立教学和思政目标、挖掘思政教学内容、构建课程思政矩阵、细化教学案例设计、强化教学效果评估等内容.将思政要素与《运输系统规划与设计》课程教学内容有机整合,形成课程思政矩阵,并列出课程思政案例,探索课程思政建设的新路径.

随着冷链物流运输技术的发展,如何在运输过程中保证货物的质量已成为当前物流研究的热点.文中以冷链运输货物为研究对象,针对冷链物流过程中车厢内温湿度异常以及货物变质等问题,基于SQL Server和Python进行质量跟踪系统的设计,用于实现运输过程中货物的数据监控.电子鼻技术能够在物流过程中对货物状态进行实时监控,并对车厢内的环境进行相对应的调整,从而在一定程度上避免货物的损耗,提高物流过程中货物的运输效率.

Chinese new urbanization requires the integration of urban and rural transportation, especially at the county level. Establishing a reliable and practical method to locate urban–rural transit hubs becomes a key issue in transportation planning. To solve the issue that existing methods lack applicability in practices due to the need for costly data collection, this study develops an application-oriented model. First, we generate basic nodes with traffic analysis zones in both urban and rural areas. Second, the node importance evaluation model is established to identify potential hub zones based on socioeconomic and topological factors. Third, we optimize the final hub locations to maximize the served population and urban transit accessibility. To reflect passenger characteristics, accessibility to opportunities is defined in terms of weighted land-use areas within a walking distance. ArcGIS software is applied to improve the efficiency of data processing and to improve the accuracy of the calculated network distances. A case study is conducted in Gaoping, China, to confirm the validity of the proposed approach. The results indicate that the optimal hub locations are effectively aligned with the practical conditions, thus improving travel convenience. The proposed approach has the potential for broader use by transport planners and policymakers in developing people-oriented and integrated urban–rural transit hubs.

文章设计农村地区末端配送服务质量的5个维度,提出末端配送服务质量与消费者购买决策行为的研究假设,采用问卷调查法收集数据,利用SPSS软件对数据进行相关性分析、 回归分析,讨论不同维度末端配送服务质量对农村消费者网购行为的影响程度.以期研究成果为改善农村电子商务末端配送服务质量、 优化配送模式提供决策依据.

采用碳排放系数法测算物流企业运输环节移动源燃烧碳排放量,定义消耗电量转换法测算仓储环节固定源燃烧碳排放量;选取资本存量、劳动力、能源消耗作为碳排放效率评价的投入指标,产值作为期望产出,碳排放量作为非期望产出指标;采用Super-SBM模型(Super-efficiency Slack-based Measurement Model)得到技术效率、纯技术效率和规模效率这3个指标值,用以评价物流企业碳排放效率,以顺丰股份有限公司2013—2020年的营运数据为例进行实证分析.结果显示:2013—2016年、2020年的技术效率均大于1,表示资源分配达到最优状态;2017—2019年均小于1,主要是因为规模效率值偏低,2017年起顺丰提升运输业务量,逐渐调整规模效率,2020年的技术效率有了提升,找到了相对合理的平衡点.2013—2020年的纯技术效率值均大于1,近些年顺丰不断寻求技术提升以提高生产效率水平.结果表明,所提物流企业碳排放量测算方法可行,采用的Super-SBM模型能有效评价物流企业碳排放效率.最后提出物流企业需要从创新生产技术、节能减排技术等方面提升碳排放效率的改进措施.

将思政理念、元素融入到《运输系统规划与设计》课程设计环节中,提出课程思政技术路线,具体包含:确立教学和思政目标、选择思政教学环节、构建课程思政矩阵、设计思政教学环节、评估思政教学效果.将思政要素与《运输系统规划与设计》课程设计环节进行有机整合,形成课程思政矩阵,并列出课程思政案例,探索课程思政建设的新路径.

随着信息技术与管理系统的逐步高效融合,冷链物流配送车辆管理也迫切需要信息系统的支持.文中在绿色物流、低碳运输需求背景下,以冷链物流配送车辆为研究目标,分析其管理信息系统需求.将RFID电子标签、GPS定位监控、GPRS移动通讯等技术相融合,运用Matlab程序设计语言和SQL Server数据库,设计了绿色冷链物流配送车辆管理信息系统,从而实现配送车辆的实时监控和智能调度,降低碳排放损耗,提高物流企业的效率和效益.

城市末端配送网点的布局是否合理,对企业的持续发展产生了影响.文中分析网点布局的影响因素和布局优化策略,建立基于CFLP的末端配送网点优化数学优化模型;以A快递公司上海城区网点为例,选取其中11个网点做实例分析,应用所提模型进行网点布局优化,利用MATLAB工具实现遗传算法求解网点布局优化模型,最后输出结果验证了所提模型的可行性与有效性.研究内容以期提高城市末端配送效率,降低末端配送成本.

文章以SERVQUAL和LSQ服务质量评价模型为基础,考虑农村物流末端配送特点,建立由5个维度15个指标构成的农村电商物流服务质量评价体系,构建农村物流服务质量评价的集对分析模型;以某物流公司为例进行实证分析,综合评价其服务质量,并提出提升对策.

在环境压力、能源压力的大背景下,大力推动电动物流车行业的发展将会为我国带来良好的环境、经济、社会效益.目前推动电动物流车发展的主要动力是补贴和路权,但随着新能源物流车补贴大幅退坡并即将全面取消,未来路权才是推动电动物流车发展的核心力量.作为影响未来电动物流车发展的关键因素,路权政策在电动物流车产业良性发展中充当着重要角色.文中分析了我国各城市电动物流车路权政策的开放程度,依据开放程度的差异,对已经执行路权政策的城市进行归类,总结了不同开放程度的路权政策特点,并借鉴国外经验,提出针对我国电动物流车路权开放的政策建议.

同城O2O外卖配送的管理与优化是当前外卖行业亟待解决的问题.依据外卖配送特点、配送时间、餐损控制等约束,建立以最大顾客满意度为优化目标的配送路径优化模型,综合运用GIS技术得到具有可视化、科学性和高效率的优化决策;以某大学校区O2O外卖路径为研究对象验证所建模型的可行性,分别以顾客满意度最大化为优化目标、最短路径为优化目标进行路径优化,加以对比分析,结果显示GIS技术能够提供很好的解决方案.同城O2O外卖配送路径的优化可以方便消费者,降低成本,提高餐饮业的整体服务水平.

Limited-stop service is useful to increase operation efficiency where the demand is unbalanced at different stops and unidirectional. A mixed scheduling model for limited-stop buses and normal buses is proposed considering the fleet size constraint. This model can optimize the total cost in terms of waiting time, in-vehicle time and operation cost by simultaneously adjusting the frequencies of limited-stop buses and normal buses. The feasibility and validity of the proposed model is shown by applying it to one bus route in the city of Zhenjiang, China. The results indicate that the mixed scheduling service can reduce the total cost and travel time compared with the single scheduling service in the case of unbalanced passenger flow distribution and fleet constraints. With a larger fleet, the mixed scheduling service is superior. There is an optimal fleet allocation that minimizes the cost for the system, and a significant saving could be attained by the mixed scheduling service. This study contributed to the depth analysis of the relationship among the influencing factors of mixed scheduling, such as fleet size constraint, departure interval and cost.

将思政理念融入到课程教学中,以 《物流信息系统设计》 为例,改革课堂教学模式,具体包含:确定教学目标和思政目标、 梳理教学大纲、 挖掘思政教学内容、 创新教学方法、 加强教学设计、 强化教学评价等内容.将思政要素注入 《物流信息系统设计》 的课程内容,形成课程思政矩阵,并列出课程思政案例,以期为促进课程思政建设提供一些思路.

目的是对国内外可重复使用外卖餐盒设计与回收的研究成果做归纳分析,提出未来研究方向,以期为相关研究提供参考,推进可持续发展.利用文献研究法分析目前外卖餐盒存在的问题,对国内外在外卖餐盒的制作材料、包装回收利用政策、可重复使用外卖餐盒的使用、外观设计、回收方式、推广方面的研究进行综述.得出结论:目前我国可重复使用外卖餐盒的回收还停留在理论研究,还没有应用的实例,提出的可重复使用外卖餐盒回收方式并没有得到大众广泛的认可.亟待研究出兼顾外卖商家、配送员、消费者三方的需求的回收方式以及有效的推广措施并进行实践验证,提出相应措施保障可重复使用外卖餐盒的使用与回收,以促进可重复使用外卖餐盒回收方式在未来可以广泛使用.

我国正处于发展新能源汽车产业,践行绿色环保可持续发展战略的阶段,电动物流车作为新能源汽车的重要细分市场,推动电动物流车发展就显得尤为重要.相对于许多发达国家而言,我国的新能源汽车产业发展并未成熟,发展政策上还存在一些缺陷.而欧美等地区的许多发达国家早已颁布了一系列激励新能源汽车产业发展的政策,并且投入许多社会资源去确保政策的可执行性,在新能源汽车产业已经拥有了较为成熟完备的商业模式.文中分析国外电动汽车发展较为先进国家的激励政策与实施结果,借鉴国外发展新能源汽车政策的经验,对我国电动物流车的发展政策提出完善建议.