基于整体规划框架的区域综合货运规划建模方法研究

区域一体化快速发展背景下，如何系统谋划区域综合交通运输规划，使经济、土地/空间及交通与环境协调发展，是实现区域可持续发展亟需深入研究的课题。因此，本文统筹考虑经济、 土地/空间及交通和环境要素的动态交互关系，提出大区域综合货运整体规划模型的设计与开发方法。利用PECAS(Production, Exchange and Consumption Allocation System)理论框架分析生产者、消费者、交换商品、土地(空间)和运输方式之间的交互关系，并通过PECAS的集计经济流表设计模型结构，构建相应的宏观经济预测、社会经济活动空间分配、空间开发以及交通运输需求预测这4个模块，模拟区域社会经济活动增长及其空间分布与土地/空间开发及综合交通需求时变等特征之间的互动耦合关系。在社会经济发展目标、土地空间和环境等约束条件下，通过构建空间经济模型和综合交通一体化网络分配模型，实现面向多货品和多方式的综合货运整体规划建模方法，以辅助区域产业布局、土地利用与综合交通系统的整体规划。本文以长江经济带为研究案例，基于相关数据构建相应的大区域综合货运整体规划模型，分析评估2012—2035年模型预测结果。结果显示，预测得到的综合交通年平均日货运量拟合优度超过85%，分担率误差低于1%， 验证了本文建模方法的有效性。     

Seventy Minutes Plus or Minus 10 — A Review of Travel Time Budget Studies

Abstract

This paper provides a comprehensive review of travel-time budget (TTB) studies in the literature for about the past four decades. Starting with the concept of TTBs, it discusses both the studies that support the existence of TTB and also those that deem the concept to be unfounded. Sociodemographic variables and their relation to TTB are also discussed briefly. However, as past studies use different data sources, survey techniques, and methodology for analysis, cross comparison of studies is not possible. Most importantly, the underlying cause of the regularity that is found at an aggregate level is still not known. The idea of TTB is important because, if it exists, it would mean that the total time spent on travelling per person per day will remain unchanged in spite of all improvements to transport. TTB has immense implications for transport policies and it is usually ignored. The paper also explores the available theoretical explanation of this concept, past research gaps and new analysis potentials. Recent directions in TTB studies are also discussed together with the potential use of multiday multiyear panel data in TTB research to explore the phenomenon better than before.     

Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT

In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5?min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).