Travelers'attitudes toward carpooling in Lahore:motives and constraints

Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.

A RESEARCH ON URBAN RECREATIONISTS' TRAVELING BEHAVIORS IN SHANGHAI

Urban recreationists consist of those who have used urban recreational facilities, no matter they are local residents or nonlocals. Traveling models will exert impacts not only on the use of urban recreational facilities but also on the development and management of recreational resources. Taking Shanghai as a sample of destinaton and origin of urban recreationists, the author did his study based on data from comprehensive travel measurement and users' survey, including field questionnaire survey, mailing questionnaire survey, home survey, and cordon monitonng. To analyze traveing models of urban recreationists in Shanghai, the author used techniques of Recreational Activity Space (RAS) and the Use Curve (UC) Analysis. Rogarding Shanghai as an origin, the author first discussed types and characteristics of the RASs which are presented by isotourist lines in the proper and outskirts of Shanghai, the Yangtze Delta and the China's Mainland respectively. These RASs reflect the trave directional bias of Shanghainese. Then the author regarded Shanghai as a deshnahon and analyzed the RASs of tounsts from Otier provinces, which demonstrated the attractiveness of and distance decay of Shanghai. He found that a U-shaped UC model existed in the distance decay under the influence of attractiveness and distance. Finally, the author suggested, that the availability, periodicity and attractiveness, besides distance, are the basic mechanisms that affect the urban recreationists' traveling models in a city such as Shanghai.

Consumers' Travel Behavior to Shopping Centers

Based on the questionnaire data,this study analyzes the characteristics of Wuhu citizens' travel behavior in shopping centers.The following conclusions are drawn:(1)the frequency of trips to shopping centers has a great correlation with consumersJ economic level and social status;(2)the time spent to purchase goods is positively correlated with the level of goods;(3)multipurpose shopping becomes the mainstream;(4)traffic becomes the primary factor for consumers to consider;(5)the availability of supermarkets and cinemas at shopping centers affect consumers'choice of shopping centers.

Travel Patterns and Street Networks: A Novel Visualization Methodology of City-Level Traffic Flows and Network Usability

Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been of high interest to scholars. Over the years, multifaceted visualization methods have emerged to better express this travel trend from small to large scale. This study proposes a novel approach to 1) visualize city-wide travel patterns with respect to the street network orientation and 2) analyze the discrepancies between travel patterns and streets to evaluate network usability. The visualizations adopt histograms and rose diagrams to provide several insights into network-wide traffic flows. The visualization of four New York City (NYC) boroughs including Queens, Brooklyn, Bronx, and Staten Island was generated for the daily traffic and the average hourly flows in the morning and evening rush hours. Then the contrasts between built-in street network topology and travel orientation were drawn to show where people travel over the network, travel demand, and finally which segments experience high or light traffic, revealing the true picture of network usability. The findings of the study provide an insight into the novel and innovative approach that can help better understand the travel behavior lucidly and assist policymakers in decision making to maintain a balance between urban topology and travel demands. In addition, the study demonstrates how to further investigate city street networks and urbanization from different diverse dimensions.

Evidence-based practices in sustainable travel behavior intervention: A knowledge graph-based systematic review

Sustainable travel behavior intervention is an essential strategy to promote the development of urban transportation.The interventions offer personalized strategies based on certain scenario and participants to promote its effectiveness over hard travel restrictions.However,personalized strategies may also bring about difficulties to identify the actual effect of the measures.Furthermore,based on current practice,to make full use of travel behavior interventions,it is necessary to construct a unified methodological evidencebased framework to assess the reliability and effectiveness of travel behavior intervention studies.In response to these issues,we applied evidence-based knowledge graph to the field of sustainable travel behavior interventions to help decision supporters design sustainable travel behavior interventions wisely and in turn avoid excessive use of hard travel restrictions.We introduced concept of evidence-based practice to conduct a systematic analysis concerning reliability and validity of current full volume empirical studies by dimensions of scenarios,types of interventions and targets.In addition,we took advantage of high extensivity and integrability of knowledge graph to organize evidence-based related elements.Result of the systematic analysis shows that in terms of reliability of evidence,school intervention is the best scenario,knowledge incentive is the best intervention type and promoting public transit and walking proportion are the best targets.Oppositely,the reliability of interventions in workplace,belonging to reward and threat along with aiming at changing travel patterns generally and lowering travel carbon emission need to be enhanced.From the study,various research prospects are raised to promote evidence quality in the field of travel behavior intervention implementation.As a pioneer study,our research contributes to the field of urban transportation in introducing concepts of evidence-based practice and enabling optimization and extension of our achievement via the usage of knowledge graph,enhancing reliability and objectivity in urban transportation decision-making.

A comparative study of automotive trip characteristics between older drivers and others among densely inhabited district and other areas

This paper examines older driver＇s automotive trip （abbreviation： trip） characteristics which include trip frequency, trip length, destination distribution, and non- home-based （NHB） trips. A two-month experiment of 108 participants was carried out to collect GPS tracking data in Aichi Prefecture, Japan. To identify the effect of living area, a comparative analysis between older drivers and others is conducted in densely inhabited district （DID, i.e., urban） and other areas （non-DID, i.e., suburban, rural, etc）, separately. The present study found that there was no sig- nificant difference between the trip characteristics of older drivers and others who were living in DID. Thus, we suggest that the education of safety driving and the rec- ommendation of public transportation should be given to DID-living older drivers. However, the results of non-DID reflected that older drivers＇ trip frequency, trip length, destination, and NHB trips rate were shorter and lower than others＇. This implies that electric vehicles may be suit- able for promotion among older drivers in suburban and rural area. Furthermore, the regression analysis confirmed that ＂older driver＂ was a significant independent variable on trip frequency, trip length, and NHB trips, and there were interaction effects between ＂older driver＂ and ＂living areas＂ on all trip characteristics.

Transportation Users’ Attitudes and Choices of Ride-Hailing Services in Two Cities with Different Attributes

The rapid technological developments in the 21st century created new opportunities for shared-use economy applications around the globe. Among other services, Transportation Network Companies (TNCs) like Uber and Lyft emerged in the US as a transportation alternative that offered a higher level of availability, reliability, and convenience than traditional modes. However, TNCs deployment was also blamed for increases in vehicle miles traveled (VMT) in large cities that embraced TNC services early on. Concerns about TNC adoption are also magnified by the current controversy in policy and legislation as to the regulation of TNCs. These new realizations create a need to examine the transportation users’ attitudes and perceptions regarding ride-hailing service, after nearly a decade of service in the Unites States market. In doing so, this paper compares and contrasts results from two recently completed studies aiming at creating links between socio-demographic factors and TNC use. The paper describes the methods employed to collect the data and presents findings from the analysis of 790 users’ responses in the Birmingham, AL and Miami Beach, FL markets. The study documents preferences and attitudes toward TNCs and highlights similarities and differences in travel behaviors related to local considerations. Moreover, the study uses the Least Absolute Shrinkage and Selection Operator (Lasso) method to identify predictors for TNC use based on the users’ responses in Birmingham and Miami Beach case studies. Vehicle availability and waiting time emerged as the only significant predictors for the Birmingham region whereas vehicle ownership, vehicle use, residency, and prior use of transit and TNC where some of the predictors identified for the Miami Beach area. Understanding the characteristics of TNC users and the leading reasons that drive people towards the use of TNCs services is expected to help transportation agencies and TNC providers in their efforts to plan for transportation services that meet customer needs in the future.

Potential effects of automated driving on vehicle travel demand: A comparison of three case cities

Automated vehicles(AVs)hold the potential to reduce road accidents,mitigate traffic congestion,and improve travel experience.However,the possible countervailing impacts from the changes in underserved populations’vehicle travel demand tend to be overlooked.To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80,this paper explores the latent effect of AVs on vehicle kilometers traveled(VKT)in a fully AV environment using person trip survey data from the cities of Sanya,Shijiazhuang,and Shenzhen in China.This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities’latent vehicle travel demand.Results show that the average VKT of the overall population in Sanya,Shijiazhuang,and Shenzhen increased by 33.4%,47.0%,and46.8%,respectively.The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT.Integrating AVs into public transport,increasing the acceptance of automated shared mobility options,transforming road space use type,and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts.The total VKT of the overall population increased by approximately 10%-25%depending on the city.The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type Ⅱ large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

Understanding travel behavior adjustment under COVID-19

The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system.By analyzing the impact of the pandemic on the transportation system,the impact of the pandemic on the social economy can be reflected to a certain extent,and the effect of anti-pandemic policy implementation can also be evaluated.In addition,the analysis results are expected to provide support for policy optimization.Currently,most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective,while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior.Based on the license plate recognition(LPR)data,this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress,quantifies the change of travelers'spatiotemporal behaviors,and analyzes the adjustment of travelers'behaviors under the influence of the pandemic.There are three different behavior adjustment strategies under the influence of the pandemic,and the behavior adjustment is related to the individual's past travel habits.The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior.And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.

Future urban transport management

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector.However,existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems.Furthermore,emerging modes in urban mobility have not been sufficiently studied.The National Natural Science Foundation of China(NSFC)officially approved the Basic Science Center project titled“Future Urban Transport Management”in 2022.The project members include leading scientists and engineers from Beijing Jiaotong University,Beihang University,and Beijing Transport Institute.Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities,this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management.It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.

Correlation between travel experiences and post-COVID outbound tourism intention:a case study from China

The COVID-19 pandemic has devastated global tourism and recovery is proceeding very slowly.For many countries,tourism served as a major economic sector,so investigating how to recover is essential.As China was the largest source of outbound travelers before the outbreak,study of the factors influencing Chinese intentions to travel overseas in the post-COVID era is revealing.In Apr.2022,among seven provinces(or cities)with the most outbound tourists from 2019 to 2021,2450 individuals responded to a questionnaire on daily mobility,tourism experiences,and the shifts due to the pandemic.Light gradient boosting machine(LightGBM),a robust ensemble learning method,was adopted to quantify and visualize the impact of explanatory factors on outbound travel intention.In addition,the Optuna mechanism and Shapley additive explanation(SHAP)instruments were employed for tuning hyperparameters and interpreting results,respectively.Findings suggest neither one-day nor multi-day tours have resumed to pre-COVID levels.Higher frequency of multi-day tours with further destinations,less car utilization in daily shopping trips,and moderate pandemic restrictions can boost the intention to travel abroad.The concerns and desires of different age groups for overseas travel need different responses.This study reveals the factors affecting Chinese outbound travel intentions and provides suggestions for the recovery of tourism in the post-COVID period.

Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data

Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.

Evaluating COVID-19’s impacts on Puerto Rican’s travel behaviors

The COVID-19 pandemic has completely disrupted and possibly permanently changed the way humans travel.In Puerto Rico,major travel restrictions to the island have persisted at different levels since March 2020,which heavily influenced residents’travel behaviors.However,it remains unclear about how big the impact is and how inequitable it might be.The goal of this study is to evaluate COVID-19’s impacts on Puerto Rican’s travel behaviors by analyzing travel flows from Puerto Rico to the contiguous US with a modified gravity model.The roles of socioeconomic factors regarding the Puerto Rican travelers and COVID-19 factors regarding the destination US states have been assessed.COVID-19 was a strong deterring factor of travel at the beginning of the pandemic and also in the winter of 2020,but it did not keep Puerto Ricans from traveling during the summer 2020 when most travel restrictions were lifted.We found that the elderly population of Puerto Rico,despite being more vulnerable to COVID-19,were much more likely to travel during the pandemic.We also found that,during the holiday season in 2020,some socioeconomically disadvantaged populations were more likely to be traveling,a direct contradiction to their travel flows the year prior.These findings shed light on about how disproportionately affected populations behavior changed from pre-pandemic to after the pandemic started.With the continuance of the pandemic,this information is extremely valuable for future planning with respect to emergency management,travel regulation,and social benefit.

Research on the big data of traditional taxi and online car-hailing:A systematic review

The purpose of this paper is to provide a summary of a quick overview of the latest developments and unprecedented opportunities for scholars who want to set foot in the field of traditional taxi and online car-hailing(TTOC).From the perspectives of peoples(e.g.,passenger,driver,and policymaker),vehicle,road,and environment,this paper describes the current research status of TTOC's big data in six hot topics,including the ridership factor,spatio-temporal distribution and travel behavior,cruising strategy and passenger service market partition,route planning,transportation emission and new-energy,and TTOC's data extensional application.These topics were included in five mainstreams as follows:(1)abundant studies often focus only on determinant analysis on given transportation(taxi,transit,online car-hailing);the exploration of ridership patterns for a multimodal transportation mode is rare;furthermore,multiple aspects of factors were not considered synchronously in a wide time span;(2)travel behavior research mainly concentrates on the commuting trips and distribution patterns of various travel indices(e.g.,distance,displacement,time);(3)the taxi driver-searching strategy can be divided into autopsychic cruising and system dispatching;(4)the spatio-temporal distribution character of TTOC's fuel consumption(FC)and greenhouse gas(GHG)emissions has become a hotspot recently,and there has been a recommendation for electric taxi(ET)in urban cities to decrease transportation congestion is proposed;and(5)based on TTOC and point of interest(POI)multi-source data,many machine learning algorithms were used to predict travel condition indices,land use,and travel behavior.Then,the main bottlenecks and research directions that can be explored in the future are discussed.We hope this result can provide an overview of current fundamental aspects of TTOC's utilization in the urban area.

Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data

Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.

土地利用与交通规划可达性措施模型综述

Since its inception accessibility has undergone various changes in the way it is defined,measured,and modeled.The paper reviews the recent advancements made in the accessibility measures along with the models used in different applications of accessibility related to land use and transportation.The measures of accessibility are grouped under infrastructure-based,location-based,and person-based measures.The paper finds that although the person-based measures are statistically robust and theoretically sound,they are less preferred than the location-based measure in the accessibility measurement.The review finds recent development such as web based mapping and use of location based data;image mapping through convolutional neural networks;and activity-time constraints modeling in the measures of accessibility.Further,the paper reviews literature from the last five years that have used accessibility to study travel mode choices and household location choices and finds the use of three types of modeling framework-Statistical,Neural Network,and Agent Based models.Based on the literature review,this paper suggests the inclusion of environmental sustainability and gender equity in the accessibility measurement framework and a shift towards model synthesis to enhance the model accuracy and to reduce the present complexities in model building.