Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the base of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution route with and without carbon emissions cost are constructed, and fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, the path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit

Instruments of Transport Policy.

The material in this Working Paper was generated as input to DETR's Guidance on the Methodology for Multi Modal Studies (GOMMMS). DETR subsequently decided only to provide summary information on transport policy measures, and to leave the consultants involved in individual multi modal studies to make their own assessment of individual policy measures in the context of specific study areas. It has been decided to make this fuller document available as a reference source. The purpose of the review of policy measures was to provide summary information on the range of policy measures available, experience of their use and, based on past studies, their potential contribution to the range of policy objectives specified for GOMMMS. The review was based on an earlier one included in the Institution of Highways and Transportation's Guidelines on Developing Urban Transport Strategies (1996). This material was updated using references published since 1996 and expanded to cover policy measures relevant in inter-urban areas. It had been intended to circulate it for comment before publishing a revised version. However, DETR decided to use an abridged version before this consultation was complete. It should be borne in mind that this document has not, therefore, undergone the peer assessment which had been intended. To avoid unnecessary further work, the material is presented as it had been drafted for the GOMMMS Guidance document. The only modifications have been to change the chapter and paragraph numbers, and to remove the cross references to other parts of the Guidance document

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately