scholarly journals Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

2020 ◽  
Vol 10 (8) ◽  
pp. 2912 ◽  
Author(s):  
Jairo Ortega ◽  
Jamil Hamadneh ◽  
Domokos Esztergár-Kiss ◽  
János Tóth
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Daily Activity ◽  
Transport Mode ◽  
Park And Ride ◽  
Activity Chain ◽  
Private Car ◽  
Transport Modes ◽  
The Impact

The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper.

scholarly journals Travel Behavior of Car Travelers with the Presence of Park-and-Ride Facilities and Autonomous Vehicles

2021 ◽  
Author(s):  
Jamil Hamadneh ◽  
Domokos Esztergár-Kiss
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Daily Activity ◽  
Policy Makers ◽  
Park And Ride ◽  
People Search ◽  
Parking Pricing ◽  
The Impact

Travelers' behavior is predicted based on their individual preferences. People search for alternatives to maximize their benefit from doing activities, such as increasing the activity time by minimizing the travel time. Traffic congestion and the scarcity of parking spaces in the city center motivate the decision-makers to encourage travelers to use the park-and-ride (P&R) system. An evaluation concerning the impact of using the P&R system on the travel behavior of car users is conducted. Some of the existing P&R facilities are incorporated into the daily activity plans of car travelers to produce new daily activity plans (i.e., P&R facility is considered an activity). By using the Multi-Agent Transport Simulation (MATSim) open-source tool, simulations of the daily activity plans including the P&R system and autonomous vehicles (AVs) are conducted. The study examines three scenarios: (1) a simulation of the existing condition, (2) a simulation of the daily activity plans of the travelers with the P&R system, and (3) a simulation of the daily activity plans of the travelers with the P&R system and AVs. The results show that using the P&R system increases the overall travel time compared with the existing conditions, and the use of AVs as a transport mode impacts the existing modal share as follows: 64 % of the car users switch to AVs, while 15 % of the car users switch to public transport. The output of this study might be used by policy-makers in parking pricing and the location of the P&R facilities.

scholarly journals The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4163
Author(s):  
Jamil Hamadneh ◽  
Domokos Esztergár-Kiss
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Fleet Size ◽  
The Public ◽  
Levels Of Automation ◽  
Multi Agent ◽  
Transport Modes ◽  
The Impact

Introducing autonomous vehicles (AVs) on the market is likely to bring changes in the mobility of travelers. In this work, extensive research is conducted to study the impact of different levels of automation on the mobility of people, and full driving automation needs further study because it is still under development. The impacts of AVs on travel behavior can be studied by integrating AVs into activity-based models. The contribution of this study is the estimation of AVs’ impacts on travelers’ mobility when different travel demands are provided, and also the estimation of AVs’ impact on the modal share considering the different willingness of pay to travel by AVs. This study analyses the potential impacts of AVs on travel behavior by investigating a sample of 8500 travelers who recorded their daily activity plans in Budapest, Hungary. Three scenarios are derived to study travel behavior and to find the impacts of the AVs on the conventional transport modes. The scenarios include (1) a simulation of the existing condition, (2) a simulation of AVs as a full replacement for conventional transport modes, and (3) a simulation of the AVs with conventional transport modes concerning different marginal utilities of travel time in AVs. The simulations are done by using the Multi-Agent Transport Simulation (MATSim) open-source software, which applies a co-evolutionary optimization algorithm. Using the scenarios in the study, we develop a base model, determine the required fleet size of AVs needed to fulfill the demand of the different groups of travelers, and predict the new modal shares of the transport modes when AVs appear on the market. The results demonstrate that the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. The impact of the value of travel time (VOT) on the usage of AVs and the modal share is demonstrated. The decrease in the VOT of AVs increases the usage of AVs, and it particularly decreases the usage of cars even more than other transport modes. AVs strongly affect the public transport when the VOT of AVs gets close to the VOT of public transport. Finally, the result shows that 1 AV can replace 7.85 conventional vehicles with acceptable waiting time.

scholarly journals Potential Travel Time Reduction with Autonomous Vehicles for Different Types of Travellers

2021 ◽  
Vol 33 (1) ◽  
pp. 61-76
Author(s):  
Jamil Hamadneh ◽  
Domokos Esztergár-Kiss
Keyword(s):  
Travel Time ◽  
Autonomous Vehicles ◽  
Daily Activity ◽  
Travel Behaviour ◽  
Transport Models ◽  
Simulation Tools ◽  
Group A ◽  
Transport Modes ◽  
Definition Of ◽  
Group B

Autonomous Vehicles (AVs) have been designed to make changes in the travel behaviour of travellers. These changes can be interpreted using transport models and simulation tools. In this study, the daily activity plans were used to study the possibility of increasing the utility of travellers through minimizing the travel time by using AVs. Three groups of travellers were selected based on the benefits that they can obtain when AVs are on the market. The groups are (a) long-trip travellers (b) public transport riders, and (c) travellers with specified characteristics. Each group is divided into one or more scenarios based on the definition of each group and the collected data. A total of seven scenarios were derived from the collected data and simulated twice to include the existing transport modes and the presence of AVs. The simulations were conducted using Multi-Agents Transport Simulation (MATSim) that applies the concept of a co-evolutionary algorithm. MATSim simulates the current plans and the ones where AVs replace all or part of the existing conventional transport modes in the daily activity plans. The results have shown a reduction in the trip time: 13% to 42% for group (a), 33% for group (b), and 16% to 28% for group (c) compared with the original trip times. In conclusion, it can be claimed that AVs could reduce the travel time in all cases, which provides benefits for people to increase their utilities.

Quantifying the Impact of the COVID-19 Pandemic on Passenger Vehicle Drivers’ Willingness to Pay for Travel Time Savings and Reliability

2021 ◽  
pp. 036119812110388
Author(s):  
Tristan Cherry ◽  
Mark Fowler ◽  
Claire Goldhammer ◽  
Jeong Yun Kweun ◽  
Thomas Sherman ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Travel Time ◽  
Travel Behavior ◽  
Local Governments ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Travel Time Reliability ◽  
Travel Time Savings ◽  
Time Savings ◽  
The Impact

The COVID-19 pandemic has fundamentally disrupted travel behavior and consumer preferences. To slow the spread of the virus, public health officials and state and local governments issued stay-at-home orders and, among other actions, closed nonessential businesses and educational facilities. The resulting recessionary effects have been particularly acute for U.S. toll roads, with an observed year-over-year decline in traffic and revenue of 50% to 90% in April and May 2020. These disruptions have also led to changes in the types of trip that travelers make and their frequency, their choice of travel mode, and their willingness to pay tolls for travel time savings and travel time reliability. This paper describes the results of travel behavior research conducted on behalf of the Virginia Department of Transportation before and during the COVID-19 pandemic in the National Capital Region of Washington, D.C., Maryland, and Northern Virginia. The research included a stated preference survey to estimate travelers’ willingness to pay for travel time savings and travel time reliability, to support forecasts of traffic and revenue for existing and proposed toll corridors. The survey collected data between December 2019 and June 2020. A comparison of the data collected before and during the pandemic shows widespread changes in travel behavior and a reduction in willingness to pay for travel time savings and travel time reliability across all traveler types, particularly for drivers making trips to or from work. These findings have significant implications for the return of travelers to toll corridors in the region and future forecasts of traffic and revenue.

scholarly journals CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 971-980 ◽  
Author(s):  
Michal Maciejewski ◽  
Joschka Bischoff
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Traffic Volume ◽  
City Centre ◽  
Conventional Vehicle ◽  
Road Capacity ◽  
Private Car ◽  
The One ◽  
The Impact

Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.

scholarly journals By Using Model Shift and Improving The Walking Facilities: A Case Study In Kuala Lumpur, Malaysia

2014 ◽  
Vol 3 (4) ◽  
pp. 184
Author(s):  
Ali A Mohammed
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Travel Behavior ◽  
Cost Reduction ◽  
Kuala Lumpur ◽  
Private Car ◽  
Spss Software ◽  
Privately Owned ◽  
Main Attraction

The recent increase in privately owned vehicles has caused numerous problems: traffic congestions, unnecessary fuel lost and global warming are only few of these problems. This study will try to understand people’s behavior and modal choice and try to sway them to means of public transportation. A survey of mode choice between cars users in a neighborhood in Kuala Lumpur was conducted. A total of 25 surveys were collected over the course of a month. Among data collected were demographic details such as age, gender, educational level and travel behavior. The data was processed by SPSS software to determine which factors encourages and discourages using private, public transportation or walking. The study highlighted four models travel time reduction, travel cost reduction, and increase the parking charges and improves the walking facility. The sensitivity analysis results show that the main attraction that might switch private car users is travel time and improving the walking facility. The consequences of these would be less traffic on the roads contributing to less pollution and greater safety.

scholarly journals Vehicle Electrification Impacts on Energy Consumption for Different Connected-Autonomous Vehicle Scenario Runs

2019 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Ehsan Sabri Islam ◽  
Ayman Moawad ◽  
Namdoo Kim ◽  
Aymeric Rousseau
Keyword(s):  
Energy Consumption ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Transportation System ◽  
Regional Energy ◽  
Energy Impact ◽  
Potential Factors ◽  
The Impact ◽  
The City

Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Autonomous Vehicles (CAVs) will impact travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the energy consumption of electrified vehicles in the future for different penetration levels of CAVs deployment in the City of Chicago. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.

scholarly journals Can a polycentric structure affect travel behaviour? A comparison of Melbourne, Australia and Riyadh, Saudi Arabia

2014 ◽  
Vol 22 (3) ◽  
pp. 156-166 ◽  
Author(s):  
M. Alqhatani ◽  
S. Setunge ◽  
S. Mirodpour
Keyword(s):  
Travel Time ◽  
Scenario Analysis ◽  
Public Transport ◽  
Transport Model ◽  
Urban Expansion ◽  
Travel Behaviour ◽  
Private Car ◽  
The Impact ◽  
Productive Time ◽  
Public Transport Service

Abstract This study models the impact of the shift from a monocentric private-car-oriented city to polycentric public-transport-oriented city. Metropolitan areas have suffered traffic problems—in particular increase in travel time and travel distance. Urban expansion, population growth and road network development have led to urban sprawl in monocentric cities. In many monocentric cities, travel time and distance has steadily increased and is only expected to increase in the future. Excessive travel leads to several problems such as air pollution, noise, congestion, reduction in productive time, greenhouse emissions, and increased stress and accident rates. This study examines the interaction of land use and travel. A model was developed and calibrated to Melbourne and Riyadh conditions and used for scenario analysis. This model included two parts: a spatial model and a transport model. The scenario analysis included variations of residential and activity distribution, as well as conditions of public transport service.

scholarly journals Sustainable Personal Transport Modes in a Life Cycle Perspective—Public or Private?

2019 ◽  
Vol 11 (24) ◽  
pp. 7092 ◽  
Author(s):  
Rajib Sinha ◽  
Lars E. Olsson ◽  
Björn Frostell
Keyword(s):  
Life Cycle ◽  
Public Transport ◽  
Travel Behavior ◽  
Environmental Pressures ◽  
Environmental Footprints ◽  
Passenger Travel ◽  
Life Cycle Perspective ◽  
Private Car ◽  
Transport Modes ◽  
Public Versus Private

Life cycle-based studies endorse public transport to cause lower environmental pressures compared to a private car. However, a private car can cause lower environmental pressure when a public vehicle (bus or train) runs on a lower occupancy during an off-peak hour. This fact should be the basis for a more profound debate regarding public versus private transport. Many transport interventions are striving to reduce the number of car transports. To reach this goal, passengers need attractive alternatives to their reduced number of car travels (i.e., attractive public transport). This study aimed to develop a model allowing us to estimate potential environmental gains by changing travel behavior. A passenger travel model was developed based on life cycle inventories (LCI) of different travel modes to calculate environmental footprints. The model was applied in an intervention of public transport through temporary free public transport. The intervention was successful in significantly reducing the number of car transports (12%). However, total passenger kilometer travelled (PKT) increased substantially more, mainly by bus, but also train, bicycle and walking. The total energy, carbon and nitrogen oxide footprints were slightly increased after the intervention. If the commuters were assumed to travel during peak hours or the number of public transports were not affected by the increased number of commuters, the overall environmental footprints decreased. Our conclusions are that transport interventions are very complex. They may result in desired changes, but also in altered travel behavior, increasing overall impact. Thus, a very broad evaluation of all transport modes as well as potential positive social influences of the transport intervention will be necessary.

scholarly journals Exploring the correlations between spatiotemporal daily activity-travel patterns and stated interest and perception of risk with self-driving cars

2020 ◽  
Vol 1 ◽  
pp. 1-15
Author(s):  
Jingyi Xiao ◽  
Rongxiang Su ◽  
Elizabeth C. McBride ◽  
Konstadinos G. Goulias
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Daily Activity ◽  
Puget Sound ◽  
Travel Patterns ◽  
Multinomial Regression ◽  
Multinomial Regression Model ◽  
People’S Attitudes ◽  
Self Driving Cars

Abstract. The key to Autonomous Vehicles (AVs) successful penetration of markets lies in identifying specific needs that AVs satisfy for daily activity-travel participation of individuals. In this paper we explore whether and to what extent people’s exhibited spatiotemporal activity-travel patterns correlate with their stated perceptions about self-driving cars. We investigate the travel diaries of 3,411 survey respondents who live in the Puget Sound region of the U.S. in 2017 using sequence analysis. In parallel, we apply hierarchical clustering to identify people’s attitudes based on their stated interest and perception of risks about AVs. A multinomial regression model is built to examine the correlations between AV attitude clusters and daily activity-travel patterns. Statistically significant correlations are then identified. The model results suggest that people exhibiting different activity-travel behavior patterns also express distinct attitudes towards the uses of AVs. The model shows that people who travel to work during the day are more likely to be positive to AVs. In particular, the group traveling to work later than the regular 8-to-5 schedule shows stronger interest and less concerns to AVs, which can be partially explained by the diverse activities they do throughout the day, the variety of travel modes they use and presumably more schedule flexibility they need than the public transportation system offers.

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