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.

Design and Construction of a Solar Hybrid Car （Electric Motor and Human Propulsion）

This work presents the design and the construction of a solar hybrid vehicle whose principal driving force is a photovoltaic system, assisted by human propulsion. The solar race of Atacama Desert （Chile） was the In＇st challenge that the solar vehicle faced, with a maximum catchment area of 4 m^2 storage 1,500 Wh and a maximum cost of 7,000 USD. Built vehicle was tested, reaching speeds on fiat terrain of 40 km/h with engine and 50 km/h with human-powered contribution. Proposed design could be projected as a vehicle for transporting person average Latin American roads.

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.

Airport Aviation Noise Prediction Based on an Optimized Neural Network

In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.

Urban Cable——A Policy Option

In the past decade, a new generation of urban cable transport systems has emerged in many countries, most prominently in Latin America, but also in Mediterranean countries like Algeria and Turkey. Apart from being energy efficient and highly effective in bridging obstacles of all sorts, aerial ropeways also provide new access to the city for a variety of population groups. This paper displays recent cases of ropeways and gives an insight into the role that this mode can play as a part of sustainable transport systems. Some socio-political aspects are analyzed which make urban cable a politically and economically attractive policy option and conclusions are drawn from existing ropeway operations.

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

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-station status judgement-vehicle relocation”multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study.In stage one,a novel trip demand forecast model based on the long short-term memory network was established to predict users'car-pickup and car-return order volumes at each station.In stage two,a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station.Then vehicle-surplus,vehicleinsufficient,vehicle-normal stations,and the number of surplus or insufficient vehicles for each station were counted.In stage three,setting driving mileage and carbon emission as the optimization objectives,an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi.Setting 43 stations and 187 vehicles in Jiading District,Shanghai,China,as a case study,results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’car-pickup and car-return demands could be fully satisfied without any refusal.

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.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

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.