The time-dependent vehicle routing problem with soft time windows and stochastic travel times

This paper studies a vehicle routing problem with time-dependent and stochastic travel times. In our problem setting, customers have soft time windows. A mathematical model is used in which both efficiency for service as well as reliability for customers are taken into account. Depending on whether service times are included or not, we consider two versions of this problem. Two metaheuristics are built: a Tabu Search and an Adaptive Large Neighborhood Search. We carry out our experiments for well-known problem instances and perform comprehensive analyses on the numerical results in terms of the computational time and the solution quality. Experiments confirm that the proposed procedure is effective to obtain very good solutions to be performed in real-life environment.     

Predicting short-term bus passenger demand using a pattern hybrid approach

This paper proposes an Interactive Multiple Model-based Pattern Hybrid (IMMPH) approach to predict short-term passenger demand. The approach maximizes the effective information content by assembling the knowledge from pattern models using historical data and optimizing the interaction between them using real-time observations. It can dynamically estimate the priori pattern models combination in advance for the next time interval. The source demand data were collected by Smart Card system along one bus service route over one year. After correlation analysis, three temporal relevant pattern time series are generated, namely, the weekly, daily and hourly pattern time series. Then statistical pattern models are developed to capture different time series patterns. Finally, an amended IMM algorithm is applied to dynamically combine the pattern models estimations to output the final demand prediction. The proposed IMMPH model is validated by comparing with statistical methods and an artificial neural network based hybrid model. The results suggest that the IMMPH model provides a better forecast performance than its alternatives, including prediction accuracy, robustness, explanatory power and model complexity. The proposed approach can be potentially extended to other short-term time series forecast applications as well, such as traffic flow forecast.     

Passengers’ valuations of train seating layout,position and occupancy

The layout of seating within train carriages, of which there are numerous possibilities, and also the occupancy of that seating can be expected to impact on passengers’ experiences of a train journey. However, there is very little evidence on how rail passengers value different seating experiences. On the back of exploratory research, and including attitudinal evidence, this paper provides significant and original insights into rail passengers’ preferences in this area. The primary evidence base is a Stated Preference experiment, complemented by a novel Revealed Preference exercise that uses CCTV footage to observe where rail passengers prefer to sit.The valuations, expressed as travel time multipliers, obtained from the Stated Preference exercise are generally plausible and exhibit a wide range according to the precise seating configuration, the occupancy level and the seating position within a layout. The innovative Revealed Preference analysis provides an encouraging degree of support to the Stated Preference results which, reassuringly, can themselves be reconciled with related valuations widely used in the railway industry in Britain and also the findings of the exploratory and attitudinal research. Whilst we are interested here solely in obtaining valuations, we demonstrate how they can be used in demand forecasting.     

A unified-adaptive large neighborhood search metaheuristic for periodic location-routing problems

This paper introduces three variants of the Periodic Location-Routing Problem (PLRP): the Heterogeneous PLRP with Time Windows (HPTW), the Heterogeneous PLRP (HP) and the homogeneous PLRP with Time Windows (PTW). These problems extend the well-known location-routing problem by considering a homogeneous or heterogeneous fleet, multiple periods and time windows. The paper develops a powerful Unified-Adaptive Large Neighborhood Search (U-ALNS) metaheuristic for these problems. The U-ALNS successfully uses existing algorithmic procedures and also offers a number of new advanced efficient procedures capable of handling a multi-period horizon, fleet composition and location decisions. Computational experiments on benchmark instances show that the U-ALNS is highly effective on PLRPs. The U-ALNS outperforms previous methods on a set of standard benchmark instances for the PLRP. We also present new benchmark results for the PLRP, HPTW, HP and PTW.     

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.     

Partial recharge strategies for the electric vehicle routing problem with time windows

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration. We formulate this problem as a 0–1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR. These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions.     

城市快速路区间旅行时间短期预测新算法

城市道路交通状况的预测,是实现未来路况查询、车辆动态导航等智能交通系统技术的关键。该文在分析浮动车数据的时间相关性的基础上,研究城市快速路的区间旅行时间短期预测算法。首先,采用统计方法和K-NN分类法相结合的新方法对缺失数据进行填充,并利用小波变换对每天的数据进行消噪处理;其次,在分别利用时间序列模型和人工神经网络模型对城市快速路区间旅行时间进行短期预测的基础上,通过模型组合获得预测值;最后,结合北京市区二环的一段快速路区间旅行时间的历史数据和实时数据,对该文所提出的快速路区间旅行时间短期预测算法进行了评价。结果显示,该算法的预测结果的平均绝对误差百分比控制在10.43%以内,具有良好的精度。     

基于时间序列法的2014年乘用车月销量预测

对我国乘用车整体及其细分车型月度销量进行合理预测,对于企业合理安排生产,优化市场规划有着重要的意义。文章以2005年1月-2013年10月共计106个月的历史数据为依据,运用时间序列方法和StatTools工具对我国2014年度乘用车及细分车型月度销量进行了预测。预测结果准确度高,具有较高的参考价值。采用时间序列法进行预测,仅以客观数据为依据,结果更加准确合理,为整车企业安排生产提供了参考。     

公交车安全气压窗及火灾隐患监测预警器设计

针对紧急情况下公交乘客的逃生需求,设计了一种气压式安全公交车窗,当事故发生时,使车窗在3s内打开,为乘客提供及时有效的逃生通道。通过制作模型和力学分析计算,此设计既能在常规状态下保证车窗的强度和密封性,又能在紧急情况下使其迅速开启。此外,为预防事故,以AT89S52单片机为核心,采用2M007型可燃气体传感器和MQ-2型烟雾传感器建立公交车火灾监测报警系统,实时检测发动机舱及车体内可燃气体浓度和烟雾情况,具有声光报警功能。结合GSM通信模块,向公交车指挥中心传输报警信息,以便进行救援指挥。     

The interurban DRAG-Spain model: The main factors of influence on road accidents in Spain

This paper presents the results of applying DRAG methodology to the identification of the main factors of influence on the number of injury and fatal accidents occurring on Spain’s interurban network. Nineteen independent variables have been included in the model grouped together under ten categories: exposure, infrastructure, weather, drivers, economic variables, vehicle stock, surveillance, speed and legislative measures. Highly interesting conclusions can be reached from the results on the basis of the different effects of a single variable on each of the accident types according to severity. The greatest influence revealed by the results is exposure, which together with inexperienced drivers, speed and an ageing vehicle stock, have a negative effect, while the increased surveillance on roads, the improvement in the technological features of vehicles and the proportion of high capacity networks have a positive effect, since the results obtained show a significant drop in accidents.