scholarly journals A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles

2020 ◽  
Vol 12 (3) ◽  
pp. 1149 ◽  
Author(s):  
Anders F. Jensen ◽  
Thomas K. Rasmussen ◽  
Carlo G. Prato
Keyword(s):  
Large Scale ◽  
Fossil Fuels ◽  
Choice Model ◽  
Combustion Engine ◽  
Route Choice ◽  
Transport Planning ◽  
Routing Problem ◽  
Vehicle Type ◽  
Route Choice Behavior ◽  
Planning Models

Battery Electric Vehicles (BEVs) play an important role in the needed transition away from fossil fuels and Internal Combustion Engine Vehicles (ICEVs). Although transport planning models and routing problem solutions exist for BEVs, the assumption that BEV drivers search for the shortest path while constraining energy consumption does not have any empirical basis. This study presents a study of actual route choice behavior of drivers from 107 Danish households participating in a large-scale experiment with BEVs and at the same time driving their ICEVs. GPS traces from 8968 BEV and 6678 ICEV routes were map matched to a detailed road network to construct observed routes, and a route choice model was specified and estimated to capture behavioral differences related to the vehicle type. The results reveal that drivers had a higher sensitivity to travel time and trip length when driving BEVs, and to route directness after receiving the BEV, regardless of vehicle type. The results suggest the need to revise the assumptions of transport planning models and routing problems for BEVs in order not to fail to predict what drivers will do by ignoring differences and similarities related to vehicle type.

AN ANALYSIS OF TRUCKS' ROUTE CHOICE BEHAVIOR CONSIDERING VEHICLE TYPE AND TRIP LENGTH

2018 ◽  
Vol 74 (5) ◽  
pp. I_657-I_666
Author(s):  
Hideki OKA ◽  
Makoto CHIKARAISHI ◽  
Jun TANABE ◽  
Daisuke FUKUDA ◽  
Takashi OGUCHI
Keyword(s):  
Choice Behavior ◽  
Route Choice ◽  
Trip Length ◽  
Vehicle Type ◽  
Route Choice Behavior

Techno-Economic Assessment of Utilizing Wind Energy for Hydrogen Production Through Electrolysis

2017 ◽  
Author(s):  
Reza Ziazi ◽  
Kasra Mohammadi ◽  
Navid Goudarzi
Keyword(s):  
Hydrogen Production ◽  
Wind Energy ◽  
Wind Turbines ◽  
Alternative Energy ◽  
Large Scale ◽  
Fossil Fuels ◽  
Combustion Engine ◽  
Economic Assessment ◽  
Large Cities ◽  
North East

Hydrogen as a clean alternative energy carrier for the future is required to be produced through environmentally friendly approaches. Use of renewables such as wind energy for hydrogen production is an appealing way to securely sustain the worldwide trade energy systems. In this approach, wind turbines provide the electricity required for the electrolysis process to split the water into hydrogen and oxygen. The generated hydrogen can then be stored and utilized later for electricity generation via either a fuel cell or an internal combustion engine that turn a generator. In this study, techno-economic evaluation of hydrogen production by electrolysis using wind power investigated in a windy location, named Binaloud, located in north-east of Iran. Development of different large scale wind turbines with different rated capacity is evaluated in all selected locations. Moreover, different capacities of electrolytic for large scale hydrogen production is evaluated. Hydrogen production through wind energy can reduce the usage of unsustainable, financially unstable, and polluting fossil fuels that are becoming a major issue in large cities of Iran.

scholarly journals A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

2020 ◽  
Vol 7 (5) ◽  
pp. 191858
Author(s):  
S. M. Fischer ◽  
M. Beck ◽  
L.-M. Herborg ◽  
M. A. Lewis
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Choice Model ◽  
Route Choice ◽  
Gravity Models ◽  
Sampling Effort ◽  
Long Distance ◽  
General Terms ◽  
Rigorous Model ◽  
Stochastic Gravity

Human traffic along roads can be a major vector for infectious diseases and invasive species. Though most road traffic is local, a small number of long-distance trips can suffice to move an invasion or disease front forward. Therefore, understanding how many agents travel over long distances and which routes they choose is key to successful management of diseases and invasions. Stochastic gravity models have been used to estimate the distribution of trips between origins and destinations of agents. However, in large-scale systems, it is hard to collect the data required to fit these models, as the number of long-distance travellers is small, and origins and destinations can have multiple access points. Therefore, gravity models often provide only relative measures of the agent flow. Furthermore, gravity models yield no insights into which roads agents use. We resolve these issues by combining a stochastic gravity model with a stochastic route choice model. Our hybrid model can be fitted to survey data collected at roads that are used by many long-distance travellers. This decreases the sampling effort, allows us to obtain absolute predictions of both vector pressure and pathways, and permits rigorous model validation. After introducing our approach in general terms, we demonstrate its benefits by applying it to the potential invasion of zebra and quagga mussels ( Dreissena spp.) to the Canadian province British Columbia. The model yields an R 2 -value of 0.73 for variance-corrected agent counts at survey locations.

Modeling of Bicycle Route and Destination Choice Behavior for Bicycle Road Network Plan

2000 ◽  
Vol 1705 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Tetsuro Hyodo ◽  
Norikazu Suzuki ◽  
Katsumi Takahashi
Keyword(s):  
Road Network ◽  
Choice Behavior ◽  
Planning Process ◽  
Choice Model ◽  
Route Choice ◽  
Single Mode ◽  
Destination Choice ◽  
Route Choice Behavior ◽  
Road Width ◽  
The Relationship

A new modeling method that describes bicycle route or destination choice behavior is presented. Although there are numerous bicycle users in Japan, the urban transportation planning process often treats bicycles and pedestrians as a single mode. Therefore, a methodology by which to evaluate and analyze bicycle demand needs to be developed. A bicycle route choice model that describes the relationship between route choice behavior and facility characteristics (e.g., road width or sidewalk) has been proposed. This model can be applied to the planning of bicycle road networks. The data from a bicycle trip survey conducted in Japan are used to study the characteristics of the model. The model is applied to study access railway station choice (destination choice). The model can produce a better fit than can a conventional model.

Effect of Route Choice Models on Estimating Network Capacity Reliability

2000 ◽  
Vol 1733 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Anthony Chen ◽  
Maya Tatineni ◽  
Der-Horng Lee ◽  
Hai Yang
Keyword(s):  
Network Performance ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Network Capacity ◽  
Service Level ◽  
Choice Models ◽  
Transportation Systems ◽  
Traffic Demand ◽  
Route Choice Behavior

The issue of planning for adequate capacity in transportation systems to accommodate growing traffic demand is becoming a serious problem. Recent research has introduced "capacity reliability" as a new network performance index. Capacity reliability is defined as the probability that a network can accommodate a certain volume of traffic demand at a required service level given variable arc capacities, while accounting for drivers' route choice behavior. Previous papers on this topic provide a comprehensive methodology for assessing capacity reliability along with extensive simulation results. However, an important issue that remains is what type of route choice model should be used to model driver behavior in estimating network capacity reliability. Three different route choice models (one deterministic and two stochastic models) are compared, and the effect of using each of these models on estimating network capacity reliability is examined.

Dynamic Route Choice Model of Large-Scale Traffic Network

1997 ◽  
Vol 123 (4) ◽  
pp. 276-282 ◽  
Author(s):  
David E. Boyce ◽  
Der-Horng Lee ◽  
Bruce N. Janson ◽  
Stanislaw Berka
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Route Choice ◽  
Traffic Network ◽  
Dynamic Route

scholarly journals Agent-Based Route Choice with Learning and Exchange of Information

Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 58 ◽  
Author(s):  
Shanjiang Zhu ◽  
David Levinson
Keyword(s):  
Route Choice ◽  
User Equilibrium ◽  
Spatial Knowledge ◽  
Stochastic User Equilibrium ◽  
Agent Based ◽  
Route Choice Behavior ◽  
Arc Model ◽  
Planning Models ◽  
Behavioral Preferences ◽  
Levels Of Service

Planning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework.

Modeling Route Choice Behavior of Chinese Toll Road Users Based on Structural Equation Model

2011 ◽  
Vol 71-78 ◽  
pp. 4883-4886
Author(s):  
Liu Yang ◽  
Xiao Wei Wei
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Choice Behavior ◽  
Route Choice ◽  
Equation Model ◽  
Vehicle Ownership ◽  
Toll Road ◽  
Vehicle Type ◽  
Route Choice Behavior ◽  
Trip Distance

Several toll road corridors were surveyed for route choice behavior study and the structural equation model was used to analyze the route choice behavior influenced by the factors of vehicle type, vehicle ownership, trip purpose, trip distance, trip time and cost. The results suggest that vehicle type, vehicle ownership and trip distance can all have impacts on the route choice behaviors, and the influences of the trip distance and the vehicle type on the route choice behaviors are the largest.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

2011 ◽  
Vol 97-98 ◽  
pp. 925-930
Author(s):  
Shi Xu Liu ◽  
Hong Zhi Guan
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Statistical Characteristics ◽  
Traffic Information ◽  
Route Choice Behavior ◽  
The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

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