A new approach for improving the performance of freight train timetabling of a single-track railway system

A new approach for improving the performance of freight train timetabling for single-track railways is proposed. Using the idea of a fixed-block signaling system, we develop a matrix representation to express the occupation of inter- and intra-station tracks by trains illustrating the train blocking time diagram in its entirety. Train departure times, dwell times, and unnecessary stopping are adjusted to reduce average train travel time and single train travel time. Conflicts between successive stations and within stations are identified and solved. A fuzzy logic system is further used to adjust the range of train departure times and checks are made to determine whether dwell times and time intervals can be adjusted for passenger and freight trains at congested stations to minimize train waiting times. By combining manual scheduling expertise with the fuzzy inference method, timetable efficiency is significantly improved and becomes more flexible.     

基于城市文化生态下的交通系统规划设计思考

本文主要是通过分析城市不同功能的模块的内在属性,探究城市交通系统如何在适应城市文化生态系统的情况下,通过合理的规划设计,实现自身功能的最大化,其次详细的阐述了为何要将城市的文化生态系统融合于如今城市快速发展建设中融合,最后针对城市不同功能模块提出了相应有效的交通规划设计思路。     

典型航线船舶排放清单及排放特征研究

文章分析了我国典型航线典型船舶排放特征,以船舶自动识别系统数据为基础,收集整理所选研究船舶排放参数,结合船舶实际航行资料,采用动力法估算了秦皇岛港—广州港航线某散货船一个航行周期的排放清单,并分析了靠港、停泊、港内机动、巡航4个船舶状态下的排放特征,绘制了2 km×2 km分辨率的空间排放特征图。结果表明,该典型航线上典型散货船舶一个航行周期排放的SOx、NOx、PM10、PM2.5总量分别为18.88吨、30.87吨、1.85吨和1.69吨。排放源分析表明从在船舶的主机、辅机和锅炉3种排放源中,主机是主要排放源。航行状态上巡航工况排放量最大;船舶排放污染物的空间分析表明,船舶在进出港口区域是污染物排放最密集的区域。     

基于价值函数的城市绿色交通规划评价

为了评价城市绿色交通,建立了城市绿色交通评价体系,包括环保性指标等3个评价项目和道路绿化率等8个评价指标,文章通过案例分析发现,价值函数法适用于城市绿色交通评价。采用专家打分法确定指标权重系数,并对指标进行无量纲化、同趋向化等处理,采用价值函数法计算评价体系得分。     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Understanding members' carsharing (activity) persistency by using econometric model

Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under‐researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two‐stage approach microsimulates the probability of being active in any month using a binary probit model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility‐based model. The model is estimated using empirical data from one of the largest carsharing companies in North America. The model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors. Copyright © 2010 John Wiley & Sons, Ltd.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.     

Augmented Reality Vehicle system: Left-turn maneuver study

Augmented Reality “AR” is a promising paradigm that can offer users with real-time, high-quality visualization of a wide variety of information. In AR, virtual objects are added to the real-world view in real time. The AR technology can offer a very realistic environment for enhancing drivers’ performance on the road and testing drivers’ ability to react to different road design and traffic operations scenarios. This can be achieved by adding virtual objects (people, vehicles, hazards, and other objects) to the normal view while driving an actual vehicle in a real environment. This paper explores a new Augmented Reality Vehicle “ARV” system and attempts to apply this new concept to a selected traffic engineering application namely the left-turn maneuver at two-way stop-controlled “TWSC” intersection. This TWSC intersection experiment, in addition to testing the feasibility of the application, tries to quantify the size of gaps accepted by different driver’s characteristics (age and gender). The ARV system can be installed in any vehicle where the driver can see the surrounding environment through a Head Mounted Display “HMD” and virtual objects are generated through a computer and added to the scene. These different environments are generated using a well defined set of scenarios. The results from this study supported the feasibility and validity of the proposed ARV system and they showed promise for this system to be used in the field-testing for the safety and operation aspects of transportation research. Results of the left-turn maneuver study revealed that participants accepted gaps in the range of 4.0-9.0 s. This finding implies that all gaps below 4 s are rejected and all gaps above 9 s are likely to be accepted. The mean value of the left-turn time was 4.67 s which is a little bit higher than reported values in the literature (4.0-4.3 s). Older drivers were found to select larger gaps to make left turns than younger drivers. The conservative driving attitude of older drivers indicates the potential presence of reduced driving ability of elderly. Drivers’ characteristics (age and gender) did not significantly affect the left-turn time. Based on the survey questions that were handed to participants, most participants indicated good level of comfort with none or small level of risk while driving the vehicle with the ARV system. None of the participants felt any kind of motion sickness and the participants’ answers indicated a good visibility and realism of the scene with overall good system fidelity.     

Modelling the impact of weather conditions on active transportation travel behaviour

Three weather sensitive models are used to explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data are restricted to non-captive commuters who have the option of selecting among five basic modes of auto driver, auto passenger, transit, bike and walk. Daily trip rates in various weather conditions are assessed. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.