Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Traffic Scene Captioning with Multi-Stage Feature Enhancement

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images,ensuring road safety while providing an important decision-making function for sustainable transportation.In order to provide a comprehensive and reasonable description of complex traffic scenes,a traffic scene semantic captioningmodel withmulti-stage feature enhancement is proposed in this paper.In general,the model follows an encoder-decoder structure.First,multilevel granularity visual features are used for feature enhancement during the encoding process,which enables the model to learn more detailed content in the traffic scene image.Second,the scene knowledge graph is applied to the decoding process,and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again,so that themodel can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable captions.This paper reports extensive experiments on the challenging MS-COCO dataset,evaluated by five standard automatic evaluation metrics,and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods,especially achieving a score of 129.0 on the CIDEr-D evaluation metric,which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.

Sustainable urban transportation development in China:A behavioral perspective

The rapid development of economics requires highly efficient and environment-friendly urban transportation systems.Such requirement presents challenges in sustainable urban transportation.The analysis and understanding of transportation-related behaviors provide one approach to dealing with complicated transportation activities.In this study,the management of traffic systems is divided into four levels with a structural and systematic perspective.Then,several special cases from the perspective of behavior,including purchasing behaviors toward new energy vehicles,choice behaviors toward green travel,and behavioral reactions toward transportation demand management policies,are investigated.Several management suggestions are proposed for transportation authorities to improve sustainable traffic management.

High-Capacity Transport Associated with Pre- and Post-Haulage in Intermodal Road-Rail Transport

This paper develops a model for analyzing the potential of longer and heavier vehicles (LHVs) related to pre- and post-haulage in the intermodal rail-road transport chain (IRT). The paper considers the combined economic and emission costs among three different transport networks including intermodal rail-road transport with current Swedish regulatory framework for trucks, intermodal rail-road transport with LHVs, and direct-road transport. The objective is to analyse the potential of high-capacity transport associated with pre- and post-haulage for enhancing the competitiveness of intermodal transport from a full-costs perspective. The model developed is applied to a Swedish context and case study. Research findings reveal that the break-even of the IRT compared to the direct road transport could be significantly lowered, which suggests the LHVs contribute to exploring the market of IRT over smaller flows.

Usability of Waste Steel Grits in Concrete Pavement

The usability of waste steel grits and limestone sand in the construction of concrete pavement was investigated.Four different types of pavement concretes were produced,including coarse limestone concrete not containing waste steel grit,coarse limestone concrete containing waste steel grit,limestone sand concrete not containing waste steel grit,and limestone sand concrete containing waste steel grit.In this study,water/binder ratio in concrete production was taken as 0.44.In the production of limestone sand concrete containing waste steel grit,limestone sand with a grain diameter of 0.1-1.0 mm was used as aggregate.Waste steel grits with a grain diameter of 0.2-0.7 mm were added to the concrete mixture.Standard water curing and combined curing were applied to concrete samples.After standard water curing and combined curing,compression and bending tests of the same types of cube and beam concrete samples were carried out.As a result of the study,the maximum compressive and bending strengths were found to be 50.21 MPa and 5.07 MPa for limestone sand concrete containing waste steel grit.The study results show that waste steel grits increase the compressive and bending strength of concrete pavement.

Is Free Public Transportation Sustainable?

Ever wished public transportation was free? Well, if you’re in Changning City,central China’s Hunan Province, your wish just came true. Starting from July 1, local residents and visitors enjoy a free ride

Are shared electric scooters energy efficient?

Shared electric scooters(e-scooter)are booming across the world and widely regarded as a sustainable mobility service.An increasing number of studies have investigated the e-scooter trip patterns,safety risks,and environmental impacts,but few considered the energy efficiency of e-scooters.In this research,we collected the operational data of e-scooters from a major provider in Gothenburg to shed light on the energy efficiency performance of e-scooters in real cases.We first develop a multiple logarithmic regression model to examine the energy consumption of single trips and influencing factors.With the regression model,a Monte Carlo simulation framework is proposed to estimate the fleet energy consumption in various scenarios,taking into account both trip-related energy usage and energy loss in idle status.The results indicate that 40%of e-scooter battery energy was wasted in idle status in the current practice,mainly due to the relatively low usage rate(0.83)of e-scooters.If the average usage rate drops below 0.5,the wasted energy could reach up to 53%.In the end,we present a field example to showcase how to optimally integrate public transport with e-scooters from the perspective of energy efficiency.We hope the findings of this study could help understand and resolve the current and future challenges regarding the ever-growing e-scooter services.

Nonlinear relationship between urban form and transport CO_(2)emissions:Evidence from Chinese cities based on machine learning

Reducing carbon emissions from the transport sector is essential for realizing the carbon neutrality goal in China.Despite substantial studies on the influence of urban form on transport cO_(2)emissions,most of them have treated the effects as a linear process,and few have studied their nonlinear relationships.This research focused on 274 Chinese cities in 2019 and applied the gradient-boosting decision tree(GBDT)model to investigate the nonlinear effects of four aspects of urban form,including compactness,complexity,scale,and fragmentation,on urban transport CO_(2)emissions.It was found that urban form contributed 20.48%to per capita transport CO_(2)emissions(PTCEs),which is less than the contribution of socioeconomic development but more than that of transport infrastructure.The contribution of urban form to total transport CO_(2)emissions(TCEs)was the lowest,at 14.3%.In particular,the effect of compactness on TCEs was negative within a threshold,while its effect on PTCEs showed an inverted U-shaped relationship.The effect of complexity on PTCEs was positive,and its effect on TCEs was nonlinear.The effect of scale on TCEs and PTCEs was positive within a threshold and negative beyond that threshold.The effect of fragmentation on TCEs was also nonlinear,while its effect on PTCEs was positively linear.These results show the complex effects of the urban form on transport CO_(2)emissions.Thus,strategies for optimizing urban form and reducing urban transport carbon emissions are recommended for the future.