Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

Influence of land use characteristics and trip attributes on commuting mode choice: a case of Nanjing

The effects of socio-demographics, land use characteristics and trip attributes on the commute mode choice are studied with a nested logit （NL） model. Based on the random utility maximum theory, the NL model is formulated. The analysis is carried out in the main area of Nanjing. Direct and cross elasticities are calculated to analyze the effects of travel time and travel cost on the selection of travel mode choice. The results reveal that the non-motorized travel modes are more attractive in the areas with higher housing and employment accessibility and car owners are found to be more likely to commute to work by car. The bus and subway choice probabilities are more sensitive to changes in travel times than to changes in travel costs. In conclusion, a comprehensive public transit system and effective integration of land use and transportation policies help to relieve the traffic congestion levels caused by the increasing urban sprawl.

Investigation on transformation process of trip mode choice for planned special events

This paper aims to provide a decision-making method for the transportation management strategies in guiding the transformation of trip mode choice during planned special events. The Expo 2010 Shanghai is taken as an example, and a structural equation model is employed to analyze the dynamic mechanism of trip mode choice behavior and the effectiveness of the transportation management measures at different stages. Based on the difference between the objective-oriented stated preference （SP） survey results and the objectives, together with the feedback from the previous stage survey, some adjustments on the transportation management measures are made in the next stage of the planning process until the objectives are eventually achieved. The results indicate that the adjustments on transportation management measures at different stages can effectively raise the transit share to 88.6%. Nonlocal visitors are inclined to choose nonstop modes of transportation and the companion attributes have the most significant effects on the trip mode choices of visitors. The research method is proved to be an effective way to support the decision making process of transportation management measures during planned special events in the future.

Application of discrete choice model in trip mode structure forecast:a case study of Bengbu

In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.

Big Data-Driven Residents’ Travel Mode Choice Choice: A Research Overview

The research on residents'travel mode choice mainly studies how traffic flows are shared by different traffic modes,which is the prerequisite for the government to establish transportation planning and policy.Traditional methods based on survey or small data sources are difficult to accurately describe,explain and verify residents'travel mode choice behavior.Recently,thanks to upgrades of urban infrastructures,many real-time location-tracking devices become available.These devices generate massive real-time data,which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively.This paper surveys the current research status of big data-driven residents'travel mode choice from three aspects:residents'travel mode identification,acquisition of travel mode influencing factors,and travel mode choice model construction.Finally,the limitations of current research and directions of future research are discussed.

Comparative study on mode split discrete choice models

Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.

Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

In recent years,there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class.The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit(CNL) structure that allows for potential spatial correlation.The analysis uses data collected in the downtown areas of Maryland-Washington,D.C.region for shopping trips,considering household,individual,land use,and travel-related characteristics.The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level,indicating that the alternatives have high spatial correlation for the short shopping distance.The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode.Moreover,a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area,was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching.These findings have important implications for transportation demand management and urban planning.

Combinatorial Model Involving Stochastic Choices of Destination, Mode and Route

Traffic assignment models are one of the basic tools for the analysis and design of transportation systems. However, the existing models have some defects. Considering the characteristics of Chinese urban mixed traffic and the randomness of transportation information, the author develops a combinatorial model involving stochastic choices of destination, mode and route. Its uniqueness and equivalance are also proved by the optimization theory.

A Freight Mode Choice Analysis Using a Binary Logit Model and GIS: The Case of Cereal Grains Transportation in the United States

Mode choice is important in shipping commodities efficiently. This paper develops a binary logit model and a regression model to study the cereal grains movement by truck and rail in the United States using the publically available Freight Analysis Framework (FAF2.2) database and U.S. highway and networks and TransCAD, a geographic information system with strong transportation modeling capabilities. The binary logit model and the regression model both use the same set of generic variables, including mode split probability, commodity weight, value, network travel time, and fuel cost. The results show that both the binary logit and regression models perform well for cereal grains transportation in the United States, with the binary logit model yielding overall better estimates with respect to the observed truck and rail mode splits. The two models can be used to study other commodities between two modes and may produce better results if more mode specific variables are used.

Factors Affecting Mode Choice of Work Trips in Developing Cities—Gaza as a Case Study

The lack of efficient application of transportation planning process in developing cities, such as Gaza, leads to deficiency in adopting the suitable transport policies to mitigate the transportation problems resulting from urbanization and rapid increase of population. The mode choice model is probably the most important element in transportation planning and policy making. The aim of this study is to develop mode choice model for work trips in Gaza city and therefore investigating the factors that affect the employed people’s choice for transport modes. The model was developed using about two thirds of 552 questionnaires distributed for this purpose. The rest remaining third of questionnaires were used to validate the chosen models. The results of this research show that the factors that significantly affect the choice of transport modes are: total travel time, total cost divided by personal income, ownership of means of transport, distance, age, and average family monthly income. The developed model is able to predict the choice behavior of employed people in Gaza city as it is valid at 95% confidence level. This study can be used by transportation planners to predict the employed people’s behavior and travel demand analysis. The developed model can be used for predicting the future modal split by inputting predicted future value of exploratory variables.

Tour Level Mode Choice Analysis of Madison Area in Wisconsin

This study is to investigate what factors and how they affect tours (trip chains) behavior. The key issue is the understanding and definition of tour and tour level mode. Also, these definitions should fit for the data. A semi-home based tour definition is stated, and a competing mode based tour mode is defined. Based on the definition, this study used Madison Area Data from National Household Survey to estimate a MNL structured model. It is found that travel distance could be a positive factor for car mode. Meanwhile, the number of trips is also a positive factor for choosing car.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Suburban travel mode choice considering transit accessibility of stops in public transport

To analyze the influence of the transit accessibility of stops on the travel mode choices of suburban residents,the number of the lines passing by the stops within an accessible range of the resident origin and destination(OD)points and the average waiting time are used as the indexes of the transit accessibility of stops.Due to the correlation between travel time and accessible range,the transit accessibility of stops is contrasted as piecewise variables constrained by travel time.Taking the Jimei District of Xiamen,China,as an example,a binary logistic regression model of the suburban travel mode choice is constructed.The results show that it is necessary to construct transit accessibility of stops as piecewise variables.With a higher transit accessibility of stops,more residents will choose public transport.The choice of the travel mode is correlated with family attributes and personal characteristics.Morning and evening peak hours and travel distance have little effect on the choice of travel mode.Compared with the travel in urban areas,residents often chose public transport for travel within the suburbs.This research provides a basis for encouraging public transportation priority policies and decision making for transport planners in the suburbs.

Kriging Geostatistical Methods for Travel Mode Choice: A Spatial Data Analysis to Travel Demand Forecasting

This paper aims to compare the results of two techniques of Kriging (Ordinary Kriging and Indicator Kriging) that are applied to estimate the Private Motorized (PM) travel mode use (car or motorcycle) in several geographical coordinates of non-sampled values of the concerning variable. The data used was from the Origin/Destination and Public Transportation Opinion Survey, carried out in 2007/2008 at S?o Carlos (SP, Brazil). The techniques were applied in the region with 110 sample points (households). Initially, Decision Tree was applied to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be used in Ordinary Kriging. For application of Indicator Kriging it was used the variable “main travel mode” in a discrete manner, where “1” represented the use of PM travel mode and “0” characterized others travel modes. The results obtained by the two spatial estimation techniques were similar (Kriging maps and cross-validation procedure). However, the Indicator Kriging (KI) obtained the highest number of hit rates. In addition, with the KI it was possible to use the variable in its original form, avoiding error propagation. Finally, it was concluded that spatial statistics was thriving in travel demand forecasting issues, giving rise, for the both Kriging methods, to a travel mode choice surface on a confirmatory way.

A tour-based analysis of travel mode choice accounting for regional transit service

The aim of this work is to explore the impact of regional transit service on tour-based commuter travel behavior by using the Bayesian hierarchical multinomial logit model, accounting for the spatial heterogeneity of the people living in the same area.With two indicators, accessibility and connectivity measured at the zone level, the regional transit service is captured and then related to the travel mode choice behavior. The sample data are selected from Washington-Baltimore Household Travel Survey in 2007,including all the trips from home to workplace in morning hours in Baltimore city. Traditional multinomial logit model using Bayesian approach is also estimated. A comparison of the two different models shows that ignoring the spatial context can lead to a misspecification of the effects of the regional transit service on travel behavior. The results reveal that improving transit service at regional level can be effective in reducing auto use for commuters after controlling for socio-demographics and travel-related factors.This work provides insights for interpreting tour-based commuter travel behavior by using recently developed methodological approaches. The results of this work will be helpful for engineers, urban planners, and transit operators to decide the needs to improve regional transit service and spatial location efficiently.

The Strategic Choice of Talent Training Mode With The Direction of Made in China 2025

With the integration of depth technology Made in China 2025 has established an production pattern of information and personalization,it needs complex and innovative workers,which has brought severe challenges to talent training mode.Changing the thought of talent cultivation,improving the professional setting and construction,optimizing course offered mode,innovating teaching organization form,speeding up the cooperative education mode,reconstructing teachers team,accelerating the establishment of personnel training laws and regulations,these are the positive strategic choices for China to face challenges.

CHOICE管理模式对慢性胃炎患者依从性的影响

目的观察基于CHOICE管理模式的慢性胃炎患者管理模式对慢性胃炎患者依从性的影响。方法采用便利抽样的方法,纳入我院2018年1月至2019年12月收治的120例慢性胃炎患者为调查对象,采用随机数字表法将调查对象分成对照组与研究组各60例。对照组采用常规的管理方法,研究组采用CHOICE管理模式,干预6个月,对两组患者干预前后的自我效能得分、健康状况调查问卷得分,干预6个月后通过内镜检查评价疗效。结果干预后,研究组自我效能量表中日常生活、健康行为、服药及遵医嘱行为得分均高于对照组[(7.94±2.00)分vs(5.40±1.44)分、(9.24±2.61)分vs.(8.39±1.98)分、(13.17±2.51)分vs.(11.83±2.15)分、(13.68±3.03)分vs.(8.03±1.97)分](P<0.05);干预后,研究组SF-36量表中生理机能、生理职能、躯体疼痛、精力、情感职能、一般健康状况、社会功能、精神健康得分均高于对照组[(75.34±12.02)分vs.(61.24±12.09)分,(90.05±12.37)分vs.(76.56±13.46)分,(74.65±10.13)分vs.(57.47±8.82)分,(77.90±11.25)分vs.(61.41±12.74)分,(83.54±13.41)分vs.(69.35±12.22)分,(79.54±12.26)分vs.(59.91±11.14)分,(88.05±13.54)分vs.(72.57±12.66)分,(81.63±12.64)分vs.(65.37±13.52)分](P<0.05);干预后,研究组总有效率86.70%,高于对照组的56.70%(P<0.05)。结论基于CHOICE管理模式的慢性胃炎管理可提高患者和自我效能,改善患者生活质量,提高治愈率,值得临床推广和应用。

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.

Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

学生通学出行的家长接送方式的选择

研究家长接送方式选择的影响因素,对于早晚高峰交通出行政策的制定具有重要意义.考虑家长、学生的个人属性与家庭属性,以及后续行程目的、生理成本与延误时间等出行属性因素,基于多项logit(multinomial logit,MNL)模型分析对于家长接送学生交通方式选择的影响因素,并通过中国海宁市居民出行数据进行验证.研究结果表明,家长年龄越大,选择步行接送的可能性越大;学生性别对于家长接送方式的选择没有显著影响;家长性别和学生年龄对接送方式具有显著影响,其中,女性接送者更偏向于选择非机动车接送,学生年龄越小,家长更偏向于选择非机动车接送;后续行程目的为上班的家长更偏向于选择机动车接送;小汽车接送者的接送时间相对较长,但该接送方式承担的生理成本和延误时间相对较少;电动车接送者承担的生理成本和时间成本都较低.依据研究结果,从家庭、学校和交通管制等维度对学生通学出行提出相关改善建议,能够为相关部门从学生通学的角度制定交通规划管理政策、方案提供指导.