Vehicle Choice Research Articles

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

Has increased prominence of translation results changed net investment hedging practice?

PurposeThe purpose of this paper is to examine the impact of greater reporting prominence of translation results following Accounting Standard Update (ASU) 2011-05 on net investment (NI) hedging practice. The authors investigate the role of increased transparency on the decision to engage in NI hedging (participation), the degree of NI hedging (level) and the hedging vehicle choice.Design/methodology/approachThis paper uses the Heckman two-stage procedure (Heckman, 1979) in the hedging choice analysis. In the first stage, the authors model the participation decision as a function of reporting transparency, translation results and other control variables. In the second stage, the authors include the Inverse Mills ratio from the first stage Probit to examine both the level and vehicle choice decisions.FindingsWhen translation is reported more prominently, the authors find an increase in the level of NI hedging and a greater likelihood of debt as the hedging vehicle, but no evidence firms are more likely to hedge. Regardless of where translation results are reported, firms facing ongoing translation losses are more likely to hedge.Research limitations/implicationsThis paper examines S&P 500 firms in the years surrounding the effective date of ASU 2011-05. The findings suggest managers respond to the increase in reporting transparency by increasing hedging for long-term risk management purposes, supporting accounting authorities’ efforts to promote other comprehensive income information transparency. The results should hold for comparable firms with similar currency exposure, size and visibility, but may not apply to smaller firms with limited translation exposure. As only about a quarter of firms with translation exposure engage in NI hedging, the primary results are based on a relatively limited number of firms.Originality/valueTo the best of the authors’ knowledge, this is the first study that examines NI hedging behavior changes following ASU-2011-05. Second, the authors are the first to explore why firms are almost equally split between derivatives and debt as their exclusive hedging vehicle.

With so many battery electric buses in operation, why are fuel cell electric buses more relevant than ever?

The bus industry has been a frontrunner in the adoption of zero emission vehicles and their operation. In 2022, zero-emission buses accounted for approximately 12.7%1 of new bus registrations in the European Union, with the uptake growing year on year. Fuel Cell Electric buses (FCEB) and Battery Electric buses (BEB) are complementary zero-emission technologies, and while both offer advantages for fleet operators, there are significant differences that can impact the choice of vehicle:• BEBs often require changes to bus depot activities to accommodate charging time and infrastructure, which can result in schedule changes and reduced asset utilisation for the operator. To gain – or improve - the range of a BEB, opportunity charging along longer routes may be required, which can impact scheduling of buses further.• With their longer range and fast refuelling time, FCEBs can be a ‘drop in’ replacement for a diesel bus and does not suffer from the infrastructure and range issues that affect the service and depot operation of BEBs.When transitioning to zero-emission buses, it is important for the operator to choose the correct technology for the specific route characteristics to ensure that the core purpose of the bus - to move passengers safely and on time - is completed without compromise.

Cognitive Biases, Attitudes, and Beliefs of Teenagers and Parents Toward Practice Driving, Vehicle Choice, and Safety Technology

Teenagers in the United States are at the highest risk of crash during the first year of independent driving. Graduated driver licensing (GDL) policies, vehicle choice, and safety technologies all have an impact on teenager safety, yet more needs to be understood about parent and teenager attitudes and beliefs that affect the choices and decisions surrounding these issues. We conducted six focus groups with teenagers enrolled in a driver education program and their parents. A qualitative thematic analysis was undertaken of the responses of these individuals directly experiencing GDL policies and selecting vehicles for the teenagers to drive. We found parents believed there was a high likelihood teenage drivers would crash and were concerned about the associated financial costs. Parents preferred visual cues for determining vehicle safety and had concerns for the impact of safety technologies on teenager driving skills. Teenagers and parents felt that logging 60 h was a measure of the character of a driver rather than an opportunity for mastering driving skills. There was a debate balancing the intrusiveness with the convenience of recording driving through smartphone applications. Teenagers wanted to learn to drive for independence, but parents did not observe actions related to these desires. Attitudes and beliefs about which vehicles were safest were heavily influenced by personal experiences and perceptions, rather than quantitative evidence, with examples of availability-, substitution-, and outcome cognitive biases. By understanding the influence of cognitive biases on driving safety decisions, community-based educational outreach programs could more effectively improve teenager driver safety.

Travel choices in (e-)moped sharing systems: Estimating explanatory variables and the value of ride fee savings

Free-floating (e-)moped sharing systems are becoming increasingly popular and provide new ways of travel in urban areas. The vehicles offer the flexibility of one-way transportation, though the downside for service providers of free-floating shared fleet systems is that their operations may result in a spatial-temporal imbalance of the vehicle distribution within the system. The potential effectiveness of user-based relocation strategies depend on the trade-offs users make between (reduced) ride fees and (prolonged) walking times. It is therefore essential to establish the trade-offs exercised by users in the context of moped sharing systems. We elicit travel preferences and assess how vehicle choice is influenced by user characteristics, travel context and alternative-specific attributes by means of a choice experiment. Our findings indicate that respondents in the Netherlands require a reduction of €0.02 of the ride fee for each additional walking minute required for accessing a vehicle. The application of our model demonstrates that vehicle availability, pricing, trip characteristics, and socioeconomic factors significantly drive adoption rates, emphasising the importance of understanding user preferences and thereby the factors shaping the acceptance and utilisation of e-moped sharing services. These findings offer essential implications for policymakers and operators, enabling them to tailor e-moped sharing services to diverse user segments as well as understanding the impact of policies, such as helmet mandates. The outcomes presented in this paper are likely to be applicable to other vehicle sharing systems with comparable design and management configurations.

Prevalence and influential factors of adopting electric motorcycles: The case of students in Hanoi, Vietnam

The development of electric motorcycles depends on the motorcycle usage rate in each country, which is crucial in forming sustainable urban development. In this study, we investigated determinants of using e-motorcycles among students in Hanoi, Vietnam. Students are the research subject due to their critical role in shaping future travel patterns. The used data were collected by surveying 290 students from three universities. We found that the prevalence of adopting electric motorcycles among students is 11%. The results of binary logit regression show that female students in Hanoi living within distances between home and school from 3 to 5 km are more inclined to utilize electric motorcycles. The perception of usefulness and environmental concern are facilitators of the adoption. However, perceived ease of use is not a significant predictor. This study plays a role as an exploratory analysis of the actual use of electric motorcycles. However, in order to have a better understanding of the motives behind the choice of electric vehicles for students and other population segments, more research in different settings is needed.

Where to park an autonomous vehicle? Results of a stated choice experiment

The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.

Shipment size and vehicle choice modeling for road freight transport: A geographical perspective

Shipment size and freight transport mode choice have been modeled previously in many studies. There are several studies on the road transport level of this subject as well. This study investigates shipment size and road freight vehicle choice from a geographical point of view. An industrial region in a province may be neighboring another province, a large metropolitan area. Then, freight vehicle choice patterns in that industrial region may differ from the rest of the province it is part of. In this research, data were obtained from Kocaeli province in Türkiye. Modeling techniques include Analysis of Covariance (ANCOVA) for shipment size choice and Discrete Choice approaches for vehicle choice. This study investigates the effects of neighboring using dummy variable and market segmentation approaches. Both approaches show that one region of Kocaeli, neighboring Istanbul, has different vehicle choice mechanisms from the rest of Kocaeli. Therefore, in shipment size and freight vehicle choice modeling or freight transportation master plans for a region, neighboring effects of other large metropolitan areas should be considered. If necessary, separate models may be required for such regions. Effects of several variables on shipment size and vehicle choice patterns are also given.

AlphaRoute: Large-Scale Coordinated Route Planning via Monte Carlo Tree Search

This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree Search (MCTS). We first partition the road network into regions and model large-scale coordinated route planning as a Markov game, where each partitioned region is treated as a player instead of each driver. Then, AlphaRoute applies a bilevel optimization framework, consisting of several region planners and a global planner, where the region planner coordinates the route choices for vehicles located in the region and generates several strategies, and the global planner evaluates the combination of strategies. AlphaRoute is built on graph attention network for evaluating each state and MCTS algorithm for dynamically visiting and simulating the future state for narrowing down the search space. AlphaRoute is capable of 1) bridging user fairness and system efficiency, 2) achieving higher search efficiency by alleviating the curse of dimensionality problems, and 3) making an effective and informed route planning by simulating over the future to capture traffic dynamics. Comprehensive experiments are conducted on two real-world road networks as compared with several baselines to evaluate the performance, and results show that AlphaRoute achieves the lowest travel time, and is efficient and effective for coordinating large-scale routes and alleviating the traffic congestion problem. The code will be publicly available.

Multivariate analysis of following and filtering manoeuvres of Motorized Two Wheelers in mixed traffic conditions

Mixed traffic conditions present a complex problem for analysis by transportation engineers and policymakers, due to the inherent heterogeneity and lane indiscipline. The chaotic arrangements make it difficult to identify and analyze basic riding behaviour such as following and filtering. Moreover, the smaller lateral dimensions of Motorized Two Wheelers (MTWs) and their higher manoeuvreability add to the difficulty. Presence of different types of vehicles in traffic mix, following the leader with or without obliqueness, filtering through tight pores between leader vehicles, etc. are some of the aspects that require special attention. In this respect, this study is aimed at investigating the attributes related to following and filtering manoeuvres of MTWs in such disordered traffic conditions. Real world data from two Indian cities was used for the analysis, which showed that the behaviour of MTWs is heavily influenced by the type of leader vehicle(s), Clear Lateral Gap (CLG), speed, etc. Safety analysis carried out using Time-To-Collision (TTC) showed that about 8.2% of the interactions were risky. Support Vector Machine (SVM) technique was used to investigate the choice of filtering based on Clear Lateral Gap (CLG) and relative speed. Moreover, analysis of the observed parameters was conducted to obtain their specific distributions based on leader vehicle type, following regime and choice of filtering. The analysis will give directions for further research on developing driving behaviour models of MTWs in mixed traffic. The results will also find potential application in traffic flow theories, safety studies, microsimulation, implementation of MTW infrastructure, etc.

Studi Penghematan Biaya Dengan Konsep Pengalihan Kendaraan Pribadi Ke Kendaraan Online Di Kota Harapan Indah Bekasi

The requirement for a smooth and decent vehicle is naturally the single longing of all metropolitan networks in Indonesia. To solve transportation problems, individuals will generally purchase their own vehicles as motor vehicles and automobiles. This is due to the disappointment of vehicle users towards public transport. This condition brings about the expansion of vehicle ownership, bringing the development of the quantity of vehicles that are not similar to the road foundation, so it tends to clog for a very long time. This research is expected to be a solution or input for vehicle user savings. The study centers on interview surveys and recently led polls on online vehicle clients and confidential vehicle administration and private vehicle owners, under the condition of planned diversion to online application vehic. compiling questionnaires using the Multi Regression analysis method with the SPSS16.0 computer program which is used systematically to draw conclusions, For utilization below 21 km every day, online vehicle determination is cheaper than private vehicles, actually for use more than 21 km every day, the choice of secret vehicles is cheaper than online vehicles

Technology advancement is driving electric vehicle adoption

Electric vehicle sales have been growing rapidly in the United States and around the world. This study explores the drivers of demand for electric vehicles, examining whether this trend is primarily a result of technology improvements or changes in consumer preferences for the technology over time. We conduct a discrete choice experiment of new vehicle consumers in the United States, weighted to be representative of the population. Results suggest that improved technology has been the stronger force. Estimates of consumer willingness to pay for vehicle attributes show that when consumers compare a gasoline vehicle to its battery electric vehicle (BEV) counterpart, the improved operating cost, acceleration, and fast-charging capabilities of today's BEVs mostly or entirely compensate for their perceived disadvantages, particularly for longer-range BEVs. Moreover, forecasted improvements of BEV range and price suggest that consumer valuation of many BEVs is expected to equal or exceed their gasoline counterparts by 2030. A suggestive market-wide simulation extrapolation indicates that if every gasoline vehicle had a BEV option in 2030, the majority of new car and near-majority of new sport-utility vehicle choice shares could be electric in that year due to projected technology improvements alone.

Sustainable behaviour at work: How message framing encourages employees to choose electric vehicles

AbstractThis paper explores the application of message framing as a management practice to promote change in employee behaviour for corporate sustainability. We conduct a field experiment in a German automotive company to test the effects of three different frames (emotional, normative and gain) on pro‐environmental actions in relation to electric vehicle choices of 170 employees. The frames are applied via two communication channels: first, via emails to remind employees about ordering a new car and second, via pop‐up notifications appearing in the online system where employees complete their orders. We find that the interventions applied in emails, but not in pop‐up notifications, have significant positive effects on electric vehicle adoption. Yet, the durability of the effects is limited. Overall, gain framing in the form of cost saving information has the longest and most powerful impact on electric car choices. Our findings have implications for workplaces where employees might not yet possess strong pro‐environmental beliefs, showing that employee sustainable behaviour can be enhanced by emphasising complementary gain motives.

Multi-objective route optimization of urban cold chain distribution using electric and diesel powered vehicles

Increasingly serious urban road congestion and environmental pollution have led some cities to impose restrictions such as limiting certain types of vehicles and the areas in which they can operate and introducing subsidies and other incentives for electric refrigerated vehicles. As a result, both internal combustion engine (ICE) refrigerated vehicles and electric refrigerated vehicles will be used together in urban cold chain distribution for a long time into the future. Most current studies are concerned with mixed vehicle type allocation or mixed vehicle routing problems. Although there are some studies that combine the two, there has been little research into the effects of low carbon emission and multiple objective optimization on distribution and routing for a mixture of vehicle types. This paper describes the creation of an optimized decision-making cold chain distribution model that includes different types of distribution vehicles and different route optimization criteria that represent the three enterprise objectives of minimal cost, minimum carbon emissions and minimum distance. The decision criteria are influenced by conflicting benefits offered by ICE and electric refrigerated vehicles as well as government subsidies for electric refrigerated vehicles and carbon emission trading by logistics enterprises. A chaotic particle swarm optimization algorithm was used to analyze a cold chain distribution system in Wuhan, Hubei Province, China. The results show that: (1) The choice of enterprise distribution models and the vehicles and routes determined are closely related to enterprise goals. To minimize total cost, ICE refrigerated trucks with less load capacity and electric refrigerated trucks with greater load capacity are preferred. To minimize carbon emissions, ICE refrigerated trucks and electric refrigerated trucks with less load capacity are preferred. To minimize total distance, ICE refrigerated trucks and electric refrigerated trucks with greater load capacity are preferred. (2) As the value of government subsidies increases, the total load capacity of electric refrigerated vehicles also increases, the distribution routes change, and total carbon emissions and total distribution costs decrease. (3) When carbon emission trading prices increase, the number of electric refrigerated vehicles gradually increases, the distribution path changes, the total distribution costs increase, and total carbon emissions decrease. Some suggestions are made for government to determine subsidies for electric refrigerated vehicles and carbon emission trading policies that will guide the choice of distribution vehicles and affect the criteria used for route optimization by cold chain logistics enterprises.

Including research on optimization planning of multi-energy complementary electric vehicle charging station

With the enhancement of environmental protection and the incentive policies issued by governments, the choice of electric vehicles as a means of transportation has gradually become a new mode, so the establishment of a perfect charging station service network is a key link and an critical guarantee for accelerating the adoption of electric vehicles (EVs). In the early stage of EV charging station construction, reasonable and scientific planning is not only related to the convenient travel of users, but also related to the effective utilization of resources. To promote the development of low-carbon economy, this paper first propose a Voronoi diagram based charging station service division approach, followed by a M/G/K queuing model based approach for evaluating the charging capacity of the EV charging station. Finally, a location and volume model is constructed and solved to determine the optimal construction plan of the EV charging station with minimum total cost for the whole society. The experimental results show that the charging station planning method proposed in this paper is economical and effectively improves the charging convenience of electric vehicle users. The reasonable location setting and capacity configuration of charging stations promote the sustainable development of the power grid in the planning area, which has certain practical significance.

Discrete/Continuous Model for Motorcycle Ownership and Utilization Across Income Groups in a Vietnamese Medium-Sized Motorcycle-Dependent City

Understanding vehicle choice and utilization is increasingly considered in addressing motorcycle-related challenges by policymakers. However, the linkage between the discrete-continuous decisions has not been examined across income groups. This study measures the interdependence between vehicle choice and utilization for income groups in a Vietnamese medium-sized city. Model estimation results show that contributing factors to households’ motorcycle ownership include individual and household characteristics and some aspects of the built environment. However, differences exist in motorcycle ownership and utilization between low- and medium-income groups. Results from this study may suggest that households may be more likely to buy an extra motorcycle as their incomes increase and this may generate more motorcycle-related issues in the future. On the positive side, we found that the non-motorized mobility resources available in the low-income group played a key role in reducing motorcycle ownership and usage. Therefore, we suggest that medium-sized cities should develop cycling infrastructure to facilitate the mobility of low-income groups as well as promote well-established travel behavior from cyclists; then, they may be less willing to switch to motorized vehicles as their income increases.

Who is Buying SUVs and Light Trucks in Montreal? A Factor and Cluster Analysis

The steady growth in light truck use and ownership in Canada is a cause for concern because it poses significant negative externalities in the form of higher fatalities, increased congestion, impacts on the environment, and infrastructure wear and tear. Understanding why drivers choose to use these vehicles is important for policymakers interested in decreasing their use. Using data from 2,203 vehicle owners in the Montreal metropolitan area, this study uses a factor-cluster analysis approach to generate five distinct groups of drivers: a uto-dependent families, pragmatic drivers, established drivers, physically active workers, and urban drivers. Identifying these unique groups can be a useful step for policymakers interested in reducing light truck ownership by influencing vehicle choice changes, mode shifts, and land use changes. Findings from this study can help transport policymakers better understand the nuanced factors that influence the choice of a light truck on Montreal’s roads.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

Nonlinear Complementarity Model for Mixed-User Equilibrium Traffic Assignment and Mode Choice of Electric and Gasoline Vehicles

In the early years of 21st century, the ever-increasing volume of greenhouse gases from fossil fuel consumption have made humans seek alternative, non-polluting fuels as an effective strategy to reduce pollution and prevent related environmental issues. Electric vehicles (EVs) are today known as one of the most effective solutions for this purpose. To inform transportation planning and policies pertinent to EVs, models are needed to capture travelers’ behavior for vehicle type and route choice and the impacts on traffic congestion. The present study proposes a mixed complementarity traffic assignment model for the networks involving both EVs and gasoline vehicles so that the demand for each vehicle class depends on the characteristics of the paths and availability of electric charging stations on the paths. To determine the demand of each class, this study applies a logit choice model, which incorporates the effect of ownership and operating costs on demand of each class under different subsidy policies. This paper further investigates the charging behavior of EVs by considering private and public charging facilities in urban areas. To this end, the complementary traffic assignment algorithm has been used to solve the mentioned assignment problem. Besides, we used a label-setting algorithm for solving the constraint shortest path problem. The results of applying the mixed-user equilibrium to Sioux Falls and Chicago sketch networks demonstrate that our proposed algorithm outperforms existing algorithms for both solution time and accuracy across multiple networks.

A Study on the Interdependence in Sustainable Mobility Tools and Home Energy Equipment Choices

Energy consumption in transportation and households are the main two sections of energy consumption in Europe. To cope with the challenges raised by energy security, sustainability, and pollution, great efforts have been made to encourage the transition from fossil fuel vehicles to electric vehicles and car sharing. In terms of households, home renewable energy equipment such as heat pumps and solar panels are promising solutions. To accelerate the diffusion of these promising and environmentally friendly technologies, an abundance of studies has investigated the factors that impact the adoption of them. Most of the existing literature considered the choice behavior of vehicle types and choice behavior of energy equipment to be exogenous to other. Charging electric vehicles at home will lead to an increase in home energy costs. It is important to understand to what extent a household’s decision to purchase new mobility tools or energy equipment is affected by the other. This paper, therefore, attempts to investigate the correlation between the choice of home energy equipment and the choice of new mobility tools. An inverted sequence stated preference experiment was designed and conducted in the Netherlands to examine the interdependence of mobility tools choice and home energy equipment choice. Two logit models were presented to investigate how the choice of mobility tools and energy equipment impact each other. Results indicate that the promoting effect between electric vehicles and solar panels is bidirectional, while the promoting effect between electric vehicles and heat pumps is unidirectional.