scholarly journals Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance

2019 ◽  
Vol 47 (5) ◽  
pp. 2233-2265 ◽  
Author(s):  
Weibo Li ◽  
Maria Kamargianni
Keyword(s):  
Impact Analysis ◽  
Mode Choice ◽  
Policy Impact ◽  
Short Term ◽  
Car Sharing ◽  
Trip Distance

Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities

2012 ◽  
Vol 35 (7) ◽  
pp. 715-736 ◽  
Author(s):  
Hooi Ling Khoo ◽  
Ghim Ping Ong ◽  
Wooi Chen Khoo
Keyword(s):  
Policy Change ◽  
Impact Analysis ◽  
Travel Demand ◽  
Price Policy ◽  
Short Term ◽  
Fuel Price

scholarly journals Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Daben Yu ◽  
Zongping Li ◽  
Qinglun Zhong ◽  
Yi Ai ◽  
Wei Chen
Keyword(s):  
Deep Learning ◽  
Information Technologies ◽  
Short Term Memory ◽  
Demand Management ◽  
Short Term ◽  
Car Sharing ◽  
Real World Data ◽  
Temporal Perspective ◽  
Analytical System ◽  
Operation Status

Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.

scholarly journals Impacts of autonomous vehicles on traffic volume: scenarios based on survey data for the City of Karlsruhe

2021 ◽  
Author(s):  
Philipp Dörflinger
Keyword(s):  
Survey Data ◽  
Autonomous Vehicles ◽  
Employment Status ◽  
Mode Choice ◽  
Baseline Scenario ◽  
Worst Case ◽  
Trip Purpose ◽  
Trip Distance ◽  
The City ◽  
Traveled Distances

Autonomous vehicles will become a significant influence in the field of traffic and transportation. To determine the possible impact of fully automated traffic, this thesis analyzes trip-pattern data for the City of Karlsruhe, Germany. Based on survey data from the year 2012, the traveled distances are calculated in Karlsruhea baseline scenario as well as two competitive scenarios: best-case and worst-case. The database is analyzed for the most emerging trip patterns in three areas of the City of Karlsruhe. Trip data, including trip distance and mode choice, are analyzed by trip purpose and individual groups (based on employment status). By modifying the average trip distance, mode choice and trip patterns based on literature reviewed information, the consequences of autonomous vehicles are estimated. The study shows, that autonomous vehicles have the potential to reduce traffic (best-case), but on the other hand, could approximately double the overall traveled vehicle distances (worst-case).

scholarly journals Forecasting the Carsharing Service Demand Using Uni and Multivariable Models

2020 ◽  
Author(s):  
Victor Aquiles Alencar ◽  
Lucas Ribeiro Pessamilio ◽  
Felipe Rooke da Silva ◽  
Heder Soares Bernardino ◽  
Alex Borges Vieira
Keyword(s):  
Short Term Memory ◽  
Meteorological Data ◽  
Absolute Error ◽  
Climatic Data ◽  
Short Term ◽  
Car Sharing ◽  
Urban Centers ◽  
Service Demand ◽  
Boosting Algorithms

Abstract Car-sharing is an alternative to urban mobility that has been widely adopted. However, this approach is prone to several problems, such as fleet imbalance, due to the variance of the daily demand in large urban centers. In this work, we apply two time series techniques, namely, Long Short-Term Memory (LSTM) and Prophet, to infer the demand for three real car-sharing services. We also apply several state-of-the-art models on free-floating data in order to get a better understanding of what works best for this type of data. In addition to historical data, we also use climatic attributes in LSTM applications. As a result, the addition of meteorological data improved the model’s performance, especially on Evo: an average Mean Absolute Error (MAE) of approximately 61.13 travels was obtained with the demand data on Evo, while MAE equals 32.72 travels was observed when adding the climatic data, the other datasets also improved but none other improved this much. For the free-floating data test, we got the Boosting Algorithms (XGBoost, Catboost, and LightGBM) got the best performance short term, the worst one has an improvement of around 22% of MAE over the next best-ranked (Prophet). Meanwhile in the long term Prophet got the best MAE result, around 22.5% better than the second-best (LSTM).

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