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 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 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 An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

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 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.

A Review on the Factors Influencing the Park-and-Ride Traffic Management Method

2014 ◽  
Vol 567 ◽  
pp. 663-668 ◽  
Author(s):  
Irfan Ahmed Memon ◽  
Napiah Madzlan ◽  
Mir Aftab Hussain Talpur ◽  
Muhammad Rehan Hakro ◽  
Imtiaz Ahmed Chandio
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Management ◽  
Demand Management ◽  
Service Research ◽  
Policy Makers ◽  
Park And Ride ◽  
Support Policy ◽  
Management Method ◽  
The City

Park-and-ride is a traffic management method of traffic congestion problem in urban areas. As an extent of total demand management, park-and-ride service (P&R service) has broadly implemented in many countries. P&R service has proven to be progressive in alleviating traffic congestion despite of complication in finding parking spaces in the city centers. The objective of this research is to discuss a model to shift car travelers’ to park-and-ride service (P&R service) and to investigate the factors which influence car travelers’ behavior. This study can support policy makers’ with useful information for future planning and development of park-and-ride service. Research outcomes will support policy-making and provide base for future study on modal choice behavior model for park-and-ride service.

scholarly journals How Congested Jakarta is? Perception of Jakarta’s Citizen on Traffic Congestion

2017 ◽  
Vol 62 (3) ◽  
pp. 141 ◽  
Author(s):  
Muhammad Halley Yudhistira ◽  
Decky Priambodo Koesrindartono ◽  
Sonny Harry Budiutomo Harmadi ◽  
Andhika Putra Pratama
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Urban Economics ◽  
Behavioral Science ◽  
Probabilistic Sampling ◽  
Golden Triangle ◽  
Business District ◽  
Physical Engineering ◽  
Sampling Survey

This paper aims to reveal the behavior and perception of Jakarta’s citizens on traffic congestion in Jakarta. Although this approach is somewhat well-developed in behavioral science, its utilization in urban economics study, is still limited. Detecting the traffic congestion and its cause mainly relies on physical (engineering) methods, i.e V/C ratio. Here, we define the traffic congestion through two variables; ordinal traffic congestion perception and proportion of expected travel time to perceived travel time. Using a non-probabilistic sampling survey held in one of densest business district in Jakarta called Sudirman-Thamrin Golden Triangle Area; the estimation results show that travel behavior plays a major role in affecting travel time perceptions.AbstrakStudi ini bertujuan untuk melihat tingkah laku masyarakat Jakarta terhadap kemacetan di Jakarta. Pendekatan yang digunakan dalam studi ini telah banyak dikembangkan dalam studi behavioral science, namun penggunaanya dalam studi ekonomi perkotaan masih terbatas. Mendeteksi tingkat kemacetan serta penyebabnya umumnya mengandalkan metode fisik seperti V/C ratio. Studi ini mendefinisikan tingkat kemacetan ke dalam dua variabel, persepsi tingkat kemacetan ordinasl serta proporsi dari ekspektasi waktu perjalanan terhadap waktu perjalanan actual. Dengan menggunakan survey non-probabilitic sampling di Sudirman-Tharim Golden Triangle Area, hasil estimasi menunjukkan bahwa perilaku perjalanan (travel behavior) berperan utama dalam mempengaruhi persepsi waktu perjalanan.Kata kunci: Tingkat Kemacetan; Waktu Perjalanan; Perilaku Perjalanan; PersepsiJEL classifications: R40; R41

The impact of heavy vehicle platoons on bridge traffic loads

2021 ◽  
Author(s):  
Matthew Sjaarda ◽  
Alain Nussbaumer
Keyword(s):  
Autonomous Vehicles ◽  
Maximum Load ◽  
Policy Makers ◽  
Traffic Loads ◽  
Weigh In Motion ◽  
Traffic Stream ◽  
Load Effects ◽  
Reduce Emissions ◽  
Vehicle Platoons ◽  
The Impact

<p>Traffic experts expect that interconnected autonomous vehicles will be implemented on roads in the near future to reduce emissions and to increase safety on roads [1], [2]. Since the navigation of vehicles in platoons is highly time synchronized, current inter-vehicle distances will decrease. Simulations have been conducted to measure the effect of platoons on bridge traffic loads in this study. Information regarding vehicle characteristics in current traffic is gathered using weigh-in- motion (WIM) technology so that synthetic traffic may be generated. Platoons are created through a “swapping” algorithm; the result is a traffic stream with platoons, and an otherwise equivalent basic traffic stream. A library of bridge influence lines is then subjected to each traffic stream to observe the effects of platoons on maximum load effects. The goal is to provide policy-makers and bridge authorities with the knowledge to make wise decisions during this transportation revolution.</p>

Exploring the impact of public transport strikes on travel behavior and traffic congestion

2018 ◽  
Vol 12 (8) ◽  
pp. 613-623 ◽  
Author(s):  
Duy Q. Nguyen-Phuoc ◽  
Graham Currie ◽  
Chris De Gruyter ◽  
William Young
Keyword(s):  
Public Transport ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
The Impact
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