scholarly journals Understanding user attitudes and economic aspects in a corporate multimodal mobility system: results from a field study in Germany

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Madlen Günther ◽  
Benjamin Jacobsen ◽  
Marco Rehme ◽  
Uwe Götze ◽  
Josef F. Krems
Keyword(s):  
Electric Vehicles ◽  
Public Transport ◽  
Positive Attitude ◽  
Business Travel ◽  
User Attitudes ◽  
Mobility System ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Electric Bicycles ◽  
Corporate Travel

AbstractThe present study aims to investigate user attitudes and behaviour when users interact with a corporate multimodal mobility sharing system, consisting of battery electric vehicles (BEVs), pedelecs (i.e. electric bicycles) and public transport. We analysed participants’ attitudes towards BEVs, pedelecs, public transport, and the underlying service tools, as well as the economic impacts of the whole corporate multimodal mobility system. Ninety-three participants took part in the 22 months long naturalistic driving study and used the corporate multimodal mobility sharing system for their business travel. The attitudes towards BEVs and the keyless access were evaluated as the most positive components, and usage behaviour was related to a more positive attitude towards pedelecs, public transport, as well as the keyless access and the booking tool. The economic evaluation revealed the possibility of significant reductions in mobility costs when integrating different means of transport into a smart multimodal mobility system. The findings may help fleet owners to further improve existing mobility concepts for corporate travel.

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

scholarly journals Implementing Electromobility in Public Transport in Poland in 1990–2020. A Review of Experiences and Evaluation of the Current Development Directions

2021 ◽  
Vol 13 (7) ◽  
pp. 4009
Author(s):  
Marcin Połom ◽  
Paweł Wiśniewski
Keyword(s):  
Electric Vehicles ◽  
Public Transport ◽  
Online Survey ◽  
Small Towns ◽  
The European Union ◽  
Operational Capabilities ◽  
Electric Bus ◽  
Low Emission ◽  
National Programs ◽  
Regional Authorities

Public transport has undergone major changes in recent years. In particular, they relate to the issue of environmental impact. Due to the significant emission of pollutants from the economy, in particular from the transport segment, member states of the European Union have taken measures to limit its scope. Only low-emission and zero-emission vehicles are to be used in transport, and mainly those that are powered by electricity in public transport. The development of battery technologies has led to a revolution in the range and operational capabilities of electric buses in the last decade. They have become a seemingly easy alternative to traditional electric vehicles in public transport—trams and trolleybuses. This article presents the possibilities and limitations of the development of public transport in Poland based on electric buses. An attempt was made to review the literature and compare the possibility of the functioning of buses, trams and trolleybuses in the Polish socio-economic, environmental and technological conditions. The article was based on a literature query, an analysis of unpublished materials, and a qualitative analysis of national programs endorsing the idea of electromobility as well as an online survey on the perception of electric public transport. The main goal of the article was to identify and evaluate the possibilities of developing public transport in Poland with the use of electric buses. The main results of the work include the demonstration that the optics of the national and regional authorities in Poland are focused mainly on electric buses without a thorough analysis of the legitimacy of their operation, especially in small towns. The incentive in the form of subsidizing the purchase of an electric bus is sufficient for them, and the future effects of using electric buses are not investigated.

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110071
Author(s):  
Yingfeng (Eric) Li ◽  
Haiyan Hao ◽  
Ronald B. Gibbons ◽  
Alejandra Medina
Keyword(s):  
Road Traffic ◽  
Wait Time ◽  
Study Data ◽  
Gap Acceptance ◽  
Factors Affecting ◽  
Unsignalized Intersections ◽  
Research Activities ◽  
Stop Sign ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
Author(s):  
Bashar Dhahir ◽  
Yasser Hassan
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Study Data ◽  
Output Analysis ◽  
Horizontal Curves ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

Analysis of near crashes among teen, young adult, and experienced adult drivers using the SHRP2 naturalistic driving study

2018 ◽  
Vol 19 (sup1) ◽  
pp. S89-S96 ◽  
Author(s):  
Thomas Seacrist ◽  
Ethan C. Douglas ◽  
Elaine Huang ◽  
James Megariotis ◽  
Abhiti Prabahar ◽  
...  
Keyword(s):  
Young Adult ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Experienced Adult
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