scholarly journals Modeling the Joint Choice Decisions on Urban Shopping Destination and Travel-to-Shop Mode: A Comparative Study of Different Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Chuan Ding ◽  
Binglei Xie ◽  
Yaowu Wang ◽  
Yaoyu Lin
Keyword(s):  
Travel Behavior ◽  
Mode Switching ◽  
Nested Logit ◽  
Model Structure ◽  
Travel Mode ◽  
Downtown Area ◽  
Mnl Model ◽  
Car Travel ◽  
The Impact ◽  
Mode A

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.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals Are People from Households with Children More Likely to Travel by Car? An Empirical Investigation of Individual Travel Mode Choices in Shanghai, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4573 ◽  
Author(s):  
Nan Ye ◽  
Linjie Gao ◽  
Zhicai Juan ◽  
Anning Ni
Keyword(s):  
Travel Behavior ◽  
Empirical Investigation ◽  
Car Ownership ◽  
Car Use ◽  
Planning Policy ◽  
Econometric Methods ◽  
Household Members ◽  
Households With Children ◽  
Car Travel ◽  
The Impact

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.

Chasing Sustainability

2015 ◽  
Vol 2531 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Myriam Langlois ◽  
Dea van Lierop ◽  
Rania A. Wasfi ◽  
Ahmed M. El-Geneidy
Keyword(s):  
Travel Behavior ◽  
Multinomial Logistic Regression ◽  
Motor Vehicles ◽  
Mode Switching ◽  
Transit Oriented Development ◽  
Travel Mode ◽  
Policy Makers ◽  
Shopping Trips ◽  
Negative Impacts ◽  
Automobile Use

One of the solutions suggested for mitigating the detrimental effect of motor vehicles on society is to implement transit-oriented development (TOD). This type of development is intended to reduce automobile use and urban sprawl as well as to provide communities with more socially, environmentally, and economically sustainable neighborhoods that offer a variety of mobility choices. This study attempted to find out whether new residents adopted more sustainable modes of transportation after their relocation to a TOD. The analysis determined which factors influenced travel mode switching decisions by specifying a multilevel multinomial logistic regression model. Data for the analysis were drawn from a travel behavior survey conducted on residents in seven North American TODs in 2013. The results showed that newcomers adopted more sustainable travel modes for amenities and leisure trips after they relocated to a TOD but that they were less likely to do so for work and shopping trips. To encourage more sustainable travel modes, the study findings suggested that transit incentives coupled with workplace parking charges needed to be considered. Factors that were found to increase the probability that new TOD residents would switch to a more sustainable mode of transportation included their awareness of the environmental impact of each travel mode, the ease with which it was possible to walk through the neighborhood and to various destinations, and the proximity to transit stops. However, larger household size, homeownership, and the addition of a new car had negative impacts. The findings provided new insights into TOD planning and its link to travel behavior; these insights could benefit planners, engineers, and policy makers who have adopted the TOD approach to development with the goal of mitigating car usage.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Modelling Agricultural Controls for Flooding and Soil Erosion

2018 ◽  
pp. 776-796
Author(s):  
Roger Moussa ◽  
Bruno Cheviron
Keyword(s):  
Spatial Distribution ◽  
Model Structure ◽  
Land Uses ◽  
Research Challenges ◽  
Water And Sediment ◽  
Flow Processes ◽  
Cultivation Practices ◽  
Modelling Strategies ◽  
The Impact ◽  
Significant Factors

Floods are the highest-impact natural disasters. In agricultural basins, anthropogenic features are significant factors in controlling flood and erosion. A hydrological-hydraulic-erosion diagnosis is necessary in order to choose the most relevant action zones and to make recommendations for alternative land uses and cultivation practices in order to control and reduce floods and erosion. This chapter first aims to provide an overview of the flow processes represented in the various possible choices of model structure and refinement. It then focuses on the impact of the spatial distribution and temporal variation of hydrological soil properties in farmed basins, representing their effects on the modelled water and sediment flows. Research challenges and leads are then tackled, trying to identify the conditions in which sufficient adequacy exists between site data and modelling strategies.

scholarly journals A note on the relations between elastic and inelastic interactions and increasing ratio σel(s)/σtot(s) at the LHC

2019 ◽  
Vol 34 (32) ◽  
pp. 1950259 ◽  
Author(s):  
S. M. Troshin ◽  
N. E. Tyurin
Keyword(s):  
Elastic Scattering ◽  
Energy Dependence ◽  
Impact Parameter ◽  
Cross Sections ◽  
Parameter Dependence ◽  
Slope Parameter ◽  
Short Notice ◽  
Inelastic Interactions ◽  
The Impact ◽  
Mode A

We comment briefly on relations between the elastic and inelastic cross-sections valid for the shadow and reflective modes of the elastic scattering. Those are based on the unitarity arguments. It is shown that the redistribution of the probabilities of the elastic and inelastic interactions (the form of the inelastic overlap function becomes peripheral) under the reflective scattering mode can lead to increasing ratio of [Formula: see text] at the LHC energies. In the shadow scattering mode, the mechanism of this increase is a different one, since the impact parameter dependence of the inelastic interactions probability is central in this mode. A short notice is also given on the slope parameter and the leading contributions to its energy dependence in both modes.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

scholarly journals Exploring the impact of COVID-19 on individual's travel mode choice in China

2021 ◽  
Vol 106 ◽  
pp. 271-280
Author(s):  
Siliang Luan ◽  
Qingfang Yang ◽  
Zhongtai Jiang ◽  
Wei Wang
Keyword(s):  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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