scholarly journals Difference in travel behavior between immigrants in the u.s. and us born residents: The immigrant effect for car-sharing, ride-sharing, and bike-sharing services

2021 ◽  
Vol 9 ◽  
pp. 100296
Author(s):  
Sangwan Lee ◽  
Michael J. Smart ◽  
Aaron Golub
Keyword(s):  
Travel Behavior ◽  
Car Sharing ◽  
Ride Sharing ◽  
Bike Sharing ◽  
Immigrant Effect ◽  
The U.S

scholarly journals Modelling and Performance Analysis of Electric Car-Sharing Systems Using Petri Nets

2020 ◽  
Vol 170 ◽  
pp. 03001 ◽  
Author(s):  
A. Hamroun ◽  
K. Labadi ◽  
M. Lazri
Keyword(s):  
Petri Nets ◽  
Simulation Analysis ◽  
Discrete Event ◽  
Stochastic Petri Nets ◽  
Car Sharing ◽  
Event Simulation ◽  
Electric Car ◽  
Bike Sharing ◽  
And Performance ◽  
Charging Stations

Car sharing systems emerged as a new answer to mobility challenges in smart and sustainable cities. Despite their apparent success, design and exploitation of such systems raise crucial strategic and operational challenges. To help planners and decision makers, simulation, analysis and optimization models are unavoidable. Based on the formal modelling and analysis power of stochastic Petri nets, this paper proposes a discrete event simulation model for electric car sharing systems for performance and analysis purposes, taking into account their complex dynamic behaviour, organization and parameters including capacities of the stations, battery and energy availability, locations of charging stations and also their car maintenance activities, not negligible compared to the case of bike-sharing systems.

scholarly journals Suburban Employment Centers: Probing the Influence of Site Features on the Journey-to-Work

1989 ◽  
Vol 8 (2) ◽  
pp. 75-85 ◽  
Author(s):  
Robert Cervero
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Low Density ◽  
Traffic Conditions ◽  
Employment Centers ◽  
Single Use ◽  
Journey To Work ◽  
Levels Of Service ◽  
The U.S

This paper argues that the low-density, single-use character of most suburban workplaces in the U.S. has contributed to worsening traffic congestion by making most workers highly dependent on their own automobiles for accessing jobs. To test this proposition, land use and transportation data are examined for fifty of the largest suburban employment centers in the nation. Differences in the share of trips made by various modes, commuting speeds, and levels of service on major thoroughfares connecting suburban centers are compared among clusters of centers. The densities, sizes, and land use mixtures of suburban workplaces are generally found to be important determinants of worker travel behavior and local traffic conditions.

scholarly journals Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China

2019 ◽  
Vol 11 (16) ◽  
pp. 4318 ◽  
Author(s):  
Mingyang Du ◽  
Lin Cheng ◽  
Xuefeng Li ◽  
Jingzong Yang
Keyword(s):  
Travel Behavior ◽  
Logistic Model ◽  
Influential Factors ◽  
Competition Effect ◽  
Multinomial Logistic Model ◽  
Significant Difference ◽  
Bike Sharing ◽  
Taxi Service ◽  
Geographical Position ◽  
The Government

In recent years, app-based third taxi service (ATTS) and free-floating bike sharing (FFBS) have become significant travel modes to satisfy travel demands of urban residents. In order to explore the mechanism of their modes selection, firstly, based on 595 valid samples, differences between ATTS and FFBS in original modes, travel distance, geographical position distribution, and travel emergency degree were compared. Then, a multinomial logistic model was established to investigate the factors influencing the choice among ATTS, FFBS, and traditional travel modes (TTM). The results show that: (1) FFBS attracts users whose original modes are walking, private bicycle and bus, while ATTS has a certain competition effect on cruising taxi and bus. (2) Residents are more likely to change from bus to FFBS on weekends, while this competitive relationship between ATTS and bus has no significant difference in different dates. (3) Compared with TTM, residents are more inclined to utilize shared modes to participate in flexible activities, especially in suburb. (4) Interestingly, ATTS is more likely to be utilized in emergency travel, and the residents without registered permanent residences tend to use FFBS and ATTS. Finally, some suggestions and policies were proposed for the government and enterprises to improve operation managements.

Smart Car Sharing

2019 ◽  
Vol 16 (8) ◽  
pp. 3285-3289
Author(s):  
S. Murugan ◽  
K. Madhu Varma ◽  
M. Y. N. Sai Prudhvi
Keyword(s):  
Urban Areas ◽  
Time Sharing ◽  
Car Sharing ◽  
Ride Sharing ◽  
Time Out ◽  
Social Productivity ◽  
The Individual ◽  
The City ◽  
Individual Dimension ◽  
Day By Day

With the tremendous increment in number of vehicles on street, individuals around the nation particularly in metro urban areas have begun confronting issue now because of increment in rush hour gridlock which added a hour or so to their day by day voyaging time. Out of couple of, one of the techniques to decrease this hopelessness of explorers is to make them share vehicles. In Existing System, With in the city or Particular Destination just The Car Services is Available. The objective of this work is to design a “car pooling,” which is a use of vehicle sharing (additionally called lift-sharing or ride-sharing) in which drivers (alone-riders) who are heading out to work alone can request individual travelers through our application. For the individuals who utilize open transport framework to go to work day by day can utilize this application to discover drivers who are heading out to a similar goal and willing to share ride. Get-together individuals into normal excursions prompts individual and social productivity. At an individual dimension, it lessens the complete voyaging cost and the driving worry also. In spite of the fact that it is less agreeable than utilizing the individual vehicle and individuals generally require more opportunity for playing out the movement, the wide acknowledgment of the mutual use portability demonstrates its feasibility, with down to earth advancements not so much investigated. By utilizing this strategy the Users can Travel Anywhere with Low expense. This won’t just dispose of the additional voyage time of travelers yet will likewise help condition by diminishing contamination and traffic on streets. This person to person communication application is additionally called toll sharing and time sharing as you will be you are imparting both to individual voyagers.

scholarly journals Health impacts of bike sharing systems in the U.S

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
David Rojas ◽  
Raeven Clockston
Keyword(s):  
Health Impacts ◽  
Bike Sharing ◽  
The U.S

scholarly journals A Survey on the Revamped Future of Transport

2020 ◽  
Vol 170 ◽  
pp. 03002 ◽  
Author(s):  
Heena Tamboli ◽  
Jyoti Malhotra ◽  
Sambhaji Sarode ◽  
Shruti Deshpande
Keyword(s):  
Machine Learning Techniques ◽  
Fuel Cost ◽  
Smartphone Applications ◽  
Market Place ◽  
Movement Activity ◽  
Car Sharing ◽  
Applied Machine Learning ◽  
Transport Services ◽  
Learning Techniques ◽  
Bike Sharing

Transport, a movement activity from home to work, studyor market place is one of the basic needs like food, shelter clothes. Nowadays, sharing and adaptive transport services are in demand due to increased traffic and rush to public transport; contributing to saving fuel, fuel cost, and above all time. Transport sharing consists of car sharing, auto-sharing and also bike sharing. . Where in, two or more individuals targeting the same location opts to share transport system. The transport sharing model, especially taxi services also adds various adaptive facilities a one-way and two-way station-based sharing system. These services help individuals to reserve their onlineseats with bus and car-sharing. This paper reviews various existing shared and effective ways of transportation. The observations include different route matching and applied machine learning techniques used by the models which are placed in working using Google maps, APIs, sensors, and Smartphone applications to get and track thedestination.In addition, the manuscript also presents the assessment taken on the communal sphere to acquire various transport facilities adopted by people and the different challenges faced by them.

Ride Substitution Using Electric Bike Sharing

2021 ◽  
Vol 5 (1) ◽  
pp. 1-28
Author(s):  
John Wamburu ◽  
Stephen Lee ◽  
Mohammad H. Hajiesmaili ◽  
David Irwin ◽  
Prashant Shenoy
Keyword(s):  
New York ◽  
Carbon Emissions ◽  
Urban Areas ◽  
Carbon Emission ◽  
Low Carbon ◽  
Capital Costs ◽  
Ride Sharing ◽  
Bike Sharing ◽  
Electric Bike ◽  
Bike Share

While ride-sharing has emerged as a popular form of transportation in urban areas due to its on-demand convenience, it has become a major contributor to carbon emissions, with recent studies suggesting it is 47% more carbon-intensive than personal car trips. In this paper, we examine the feasibility, costs, and carbon benefits of using electric bike-sharing---a low carbon form of ride-sharing---as a potential substitute for shorter ride-sharing trips, with the overall goal of greening the ride-sharing ecosystem. Using public datasets from New York City, our analysis shows that nearly half of the taxi and rideshare trips in New York are shorts trips of less than 3.5km, and that biking is actually faster than using a car for ultra-short trips of 2km or less. We analyze the cost and carbon benefits of different levels of ride substitution under various scenarios. We find that the additional bikes required to satisfy increased demand from ride substitution increases sub-linearly and results in 6.6% carbon emission reduction for 10% taxi ride substitution. Moreover, this reduction can be achieved through a hybrid mix that requires only a quarter of the bikes to be electric bikes, which reduces system costs. We also find that expanding bike-share systems to new areas that lack bike-share coverage requires additional investments due to the need for new bike stations and bike capacity to satisfy demand but also provides substantial carbon emission reductions. Finally, frequent station repositioning can reduce the number of bikes needed in the system by up to a third for a minimal increase in carbon emissions of 2% from the trucks required to perform repositioning, providing an interesting tradeoff between capital costs and carbon emissions.

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