Using on-board logging devices to study the longer-term impact of an eco-driving course

In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.     

Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

基于BP人工神经网络的滑坡预测分析

文章以汶川地震引发的滑坡为研究对象,以震中距、地震烈度、坡度、前缘高程、坡高和岩性等影响坡体稳定性的因素为切入点,利用BP人工神经网络对实际坡体的稳定性进行了预测分析。结果表明,BP人工神经网络方法能有效预测坡体的稳定情况。     

柳南高速公路改扩建工程设计方案探讨

文章结合柳州至南宁高速公路改扩建工程项目情况,分析了该改扩建项目的技术难点和重点,阐述了改扩建工程设计的总体思路,并提出了相应的改扩建工程设计方案。     

U型桥台开裂原因分析及加固方案探讨

文章结合高速公路U型桥台开裂的典型工程实例,分析了U型桥台裂缝产生的原因,提出了相应的加固处治方案,并阐述了加固方案的实施要点与处治效果。     

基于远、近期合建的折多山公路隧道总体设计方案研究

为解决特长公路隧道升级改造困难问题,以折多山公路隧道项目为依托,创新性地提出远、近期合建的总体设计方案。通过交通量预测、工程造价、运营通风及逃生救援等方面综合比较,得出可实施的合建方案： 先期建设二级公路隧道主洞,设2段非贯通平导,后期直接将二级公路隧道作为高速公路隧道右洞,并贯通二级公路隧道平导作为高速公路隧道左洞。合建方案能有效节约工程造价,降低社会耗能。     

Spatiotemporal traffic forecasting: review and proposed directions

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.     

Locating rental stations and bikeways in a public bike system

To determine the spatial distribution of rental stations and bikeways in a public bike system, this paper proposes a facility location and network design model. The model is developed as a multi-objective programing problem that considers four objectives (minimizing cyclist risk, maximizing cyclist comfort, minimizing adverse impacts on traffic and maximizing service coverage) and multiple constraints (monetary budget, network connectivity, station spacing, bikeway types, station number and value ranges of decision variables). The ε-constraint method solves the programing problem for the public bike system in Daan District, Taipei City, Taiwan. The nine non-dominated alternatives generated are all markedly better than existing locations of rental stations and bikeways. Scenario analysis results indicate that increasing the construction budget for bikeways significantly improves cyclist safety and comfort whilst increasing the adverse impact on traffic. Planners can use this model to develop public bike systems that spatially integrate rental stations and bikeway networks.     

Emission rates of intermodal rail/road and road-only transportation in Europe: A comprehensive simulation study

Intermodal rail/road transportation combines advantages of both modes of transport and is often seen as an effective approach for reducing the environmental impact of freight transportation. This is because it is often expected that rail transportation emits less greenhouse gases than road transportation. However, the actual emissions of both modes of transport depend on various factors like vehicle type, traction type, fuel emission factors, payload utilization, slope profile or traffic conditions. Still, comprehensive experimental results for estimating emission rates from heavy and voluminous goods in large-scale transportation systems are hardly available so far. This study describes an intermodal rail/road network model that covers the majority of European countries. Using this network model, we estimate emission rates with a mesoscopic model within and between the considered countries by conducting a large-scale simulation of road-only transports and intermodal transports. We show that there are high variations of emission rates for both road-only transportation and intermodal rail/road transportation over the different transport relations in Europe. We found that intermodal routing is more eco-friendly than road-only routing for more than 90% of the simulated shipments. Again, this value varies strongly among country pairs.     

Formulating cargo inventory costs for liner shipping network design

This study examines how to incorporate the inventory costs of containerized cargoes into existing liner service planning models such that the designed networks could be improved while not causing extra modeling/computational burden. Two approaches are compared: (i) not considering the inventory costs at all and (ii) incorporating the inventory costs associated with onboard time and those related to transshipment by assuming a fixed connection time. The two models are compared with the ideal model capturing the exact inventory costs on a route choice problem and a capacity planning problem based on extensive randomly generated and practical numerical experiments. The results show that: first, ignoring the inventory costs in service planning models may lead to network design with much higher costs (poor network design decisions); second, in service planning models assuming weekly frequency, the inventory costs associated with onboard time could be formulated exactly, and those related to the connection time of weekly services could be approximated by assuming fixed connection time of 3.5 days for ports with 1 day’s minimum connection time and 4.5 days for ports with 2 days’ minimum connection time.