Research on the Choice of Shared Car Travel Behavior Based on Medium Commuting Distance

Author(s):  
Yongneng Xu ◽  
Xiaotian Wang ◽  
Zhou He
CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xiaozhe Wu ◽  
Kai Zhang ◽  
Jinping Guan ◽  
Bokui Chen ◽  
Yi Zhang ◽  
...  

2018 ◽  
Vol 10 (12) ◽  
pp. 4573 ◽  
Author(s):  
Nan Ye ◽  
Linjie Gao ◽  
Zhicai Juan ◽  
Anning Ni

China is expected to have more children now that its family planning policy has been relaxed, and the influence of children on transportation and sustainability should not be neglected. This study uses econometric methods to explore the impact that the presence of children has on household car ownership, car-travel behavior of family members, and variability in their car-use frequency across weekdays and weekends. Models are estimated using multi-day travel patterns imputed from GPS-enabled smartphone data collected in Shanghai, China. Results indicate that: (1) households with children have more private cars than those without children, and the presence of preschoolers and pupils both increase families’ demand for car ownership; (2) travel behavior of people from households with children is influenced subtly by the children’s presence, which leads them to prefer to travel by car, although the presence of retired or unemployed household members can weaken that influence; and (3) car-travel frequency of individuals is significantly different between weekdays and weekends, with the presence of pupils in the household diminishing that variability and the presence of preschoolers enlarging it. Policymakers and transportation planners should be concerned about these issues and take appropriate measures.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Chuan Ding ◽  
Binglei Xie ◽  
Yaowu Wang ◽  
Yaoyu Lin

The joint choice of shopping destination and travel-to-shop mode in downtown area is described by making use of the cross-nested logit (CNL) model structure that allows for potential interalternative correlation along the both choice dimensions. Meanwhile, the traditional multinomial logit (MNL) model and nested logit (NL) model are also formulated, respectively. This study uses the 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 results of the model reveal the significant influencing factors on joint choice travel behavior between shopping destination and travel mode. A comparison of the different models shows that the proposed CNL model structure offers significant improvements in capturing unobserved correlations between alternatives over MNL model and NL model. Moreover, a Monte Carlo simulation for a group of scenarios assuming that there is an increase in parking fees in downtown area is undertaken to examine the impact of a change in car travel cost on the joint choice of shopping destination and travel mode switching. The results are expected to give a better understanding on the shopping travel behavior.


2017 ◽  
Vol 22 (S4) ◽  
pp. 10019-10029
Author(s):  
Hai-jun Li ◽  
Hong-chang Zhou ◽  
Jian-rong Feng ◽  
Xiao-hong Chen ◽  
Wei Zhang

2021 ◽  
Vol 11 (22) ◽  
pp. 11015
Author(s):  
Melika Mehriar ◽  
Houshmand Masoumi ◽  
Atif Bilal Aslam ◽  
Syed Mubasher Gillani ◽  
Tuba Suhail ◽  
...  

Urban sprawl is a particular pattern of the street network and land use. The relationship between street networks and sprawl has been discussed by urban scholars in developed and high-income countries. Nevertheless, there is a lack of research on the relationships between street connectivity and urban travel behavior, particularly among emerging markets. This paper aims to study correlations between urban mobility and street-length density as an indicator for assessing the compactness of an area by developing two hierarchical regression models and controlling for socioeconomic variables in two large Pakistani cities: Lahore and Rawalpindi. Moreover, this paper defines optimal cutoff values for street-length density and active transport. Finally, three chi-square tests were conducted to assess the differences between using different mode choices by people living in sprawled neighborhoods versus compact neighborhoods. Our findings confirm the use of different transport modes depending on the purpose of the trip (commuting or non-commuting), length of trip (within or outside the neighborhood), and starting point (sprawled neighborhood or compact area). We also find a positive correlation between street-length density around homes and commuting distance, the frequency of public transport use, and the use of private motor vehicles in commuting trips in the two cities. Street-length density around workplaces is correlated with commuting distance, the frequency of public transport use, and the use of private motor vehicles when socioeconomic variables (including age, daily activity, and monthly income) are controlled for in the two models. The behavior of Pakistani residents changes with a street-length density of 137 and 144.7 m/m2 for homes and workplaces, respectively, in terms of using active mobility.


2019 ◽  
Vol 11 (10) ◽  
pp. 2791 ◽  
Author(s):  
Eun Hak Lee ◽  
Inmook Lee ◽  
Shin-Hyung Cho ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim

This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.


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