上海市出行者信息需求分析

在出行者信息需求调查的基础上,分析了出行者获取信息方式、出行者信息需求的内容、不同出行目的下出行者信息需求的差异和出行信息获取方式的意愿,并对不同人群的信息需求特征进行了比较分析。出行者信息需求分析对于准确把握出行者信息需求特征,进而提供高水平的信息服务具有重要意义。     

关于城市主干路设置辅路条件的研究

结合实际项目经验,通过分析道路功能定位、交通需求、交通组织方式及城市主干路辅路的设置条件等,根据所建立的计算模型,在设计规范的基础上对城市主干路设置辅路的可能性条件和必要性条件进行了深入研究,以期为类似设计研究提供参考。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

基于预约需求的共享停车平台泊位分配方法

为解决在预约需求下，考虑预约时刻、时长及延时需求的共享停车分配问题，提出一种共享停车泊位分配模型. 以平台收益和停车步行距离为优化目标，将需求分为基本及延时两种情况，确定停车预约请求的分配策略. 根据模型结构，设计随机解集生成方法，利用蒙特卡洛法确定模型的最优解. 以医院停车场及周边停车场为案例，测试模型. 结果表明，模型能较好地服务于共享停车泊位的分配，实现平台收益与满足需求之间的平衡.     

Assessing the social costs of urban transport infrastructure options in low and middle income countries

ABSTRACT

This paper develops cost models for urban transport infrastructure options in situations where motorcycles and various forms of taxis are important modes of transport. The total social costs (TSCs) of conventional bus, Bus Rapid Transit (BRT), Monorail, Metro (Elevated Rail), car, motorcycle, Taxi and Uber are calculated for an urban corridor covering operator, user and external costs. Based on the parameters for a 7?km corridor in Hanoi, Vietnam, the results show the lowest average social cost (ASC) transport modes for different ranges of demand. Motorcycle might be the best option at low demand levels while conventional bus has advantages with low-medium demand. At medium demand levels, bus-based technologies and Monorail are competitive options while Metro, with a higher person capacity, is the best alternative at the highest demand levels. Compared to other modes, the ASCs of car and Taxi/Uber are greater because of high capital cost (related to vehicles) per passenger and low occupancy. Transport planners and decision makers in low and middle income countries (LMICs) can draw on the findings of this study. However, various limitations are identified and additional research is suggested.     

产业转移背景下西部运输需求的完全分解模型

研究西部综合运输系统需求驱动因素，将产业转移因素考虑在内，对原有包含三大因素的完全分解模型进行改进，构建包含经济总量、运输需求强度、产业转移和产业结构整体变动等四大因素的完全分解模型，根据2002-2015年统计数据对西部地区综合运输系统需求进行实例计算.研究结果表明，经济总量增长是西部综合运输系统需求增长的主要原因，随着经济增速放缓，产业转移促进政策的出台，运输需求强度变化和产业转移的影响逐渐显著.在产业转移背景下，西部地区应该根据产业转移发展态势，有针对性地做好综合运输系统布局，创造更好的条件承接产业转移.     

城市绿色出行内涵和评价指标体系框架研究

绿色出行发展的根本目的是为了实现城市交通可持续发展,实现出行"安全、畅通、高效、舒适、环保、节能",从而实现社会、经济、交通和环境的协调发展。本文通过对绿色出行的概念、内涵、特征和实现途径等相关理论进行解读,确定绿色出行系统的主要构成;采用计划行为理论、交通需求管理理论等多视角,对影响和制约城市绿色出行发展的关键因素进行分析和识别,并研究提出围绕保障能力、基础设施、运输装备、运营服务等方面的绿色出行评价指标体系框架。     

基于候选列车集与弹性需求的城际铁路列车开行方案优化

城际铁路列车服务水平直接影响着全天各时段旅客出行需求量。为了研究这种影响关系,获得吻合出行需求的城际列车开行方案,首先建立旅客时段出行需求与广义出行费用间的弹性需求函数,并基于给定候选列车集构造旅客出行网络,进而以最大化列车开行收益为优化目标,构建面向弹性需求的城际列车开行方案优化模型。模型结合弹性客流在出行网络上的路径选择,从候选列车集中选择开行列车,并优化其停站方案与始发时刻。在生成初始列车开行方案基础上,设计其邻域解生成策略,构建求解模型的模拟退火算法。算例优化不同分布客流的列车开行方案,结果表明:模型与算法能够获得更吻合弹性需求的列车开行方案,且有助于提高旅客服务水平与企业经济效益。     

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.