Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case

Taxi service is an important component of airport ground access, which affects the economic competitiveness of an airport and its potential positive impact on the surrounding region. Airports across the globe experience both taxi shortages and excesses due to various factors such as the airport’s proximity to the city center, timing and frequency of flights, and the fare structure. Since taxi drivers are independent entities whose decisions affect the taxi supply at airports, it is important to understand taxi drivers’ decision mechanisms in order to suggest policies and to maintain taxi demand and supply equilibrium at the airports. In this paper, New York City (NYC) taxi drivers’ decisions about airport pick-ups or cruising for customers at the end of each trip is modeled using logistic regression based on a large taxi GPS dataset. The presented approach helps to quantify the potential impacts of parameters and to rank their influence for policy recommendations. The results reveal that spatial variables (mainly related to proximity) have the highest impact on taxi drivers’ airport pickup decisions, followed by temporal, environmental and driver-shift related variables. Along with supplementary information from unstructured taxi driver interviews, the model results are used to suggest policies for the improvement of John F. Kennedy (JFK) airport’s ground access and passenger satisfaction, i.e. the implementation of taxi driver frequent airport server punch cards and a time-specific ride share program.     

Modeling adoption timing of autonomous vehicles: innovation diffusion approach

Autonomous vehicles (AVs) are expected to act as an economically-disruptive transportation technology offering several benefits to the society and causing significant changes in travel behavior and network performance. However, one of the critical issues that policymakers are facing is the absence of a sound estimation of their market penetration. This study is an effort to quantify the effect of different drivers on the adoption timing of AVs. To this end, we develop an innovation diffusion model in which individuals’ propensities to adopt a new technology such as AVs takes influence from a desire to innovate and a need to imitate the rest of the society. It also captures various sources of inter-personal heterogeneity. We found that conditional on our assumptions regarding the changes in market price of AVs over time, their market penetration in our study region (Chicago metropolitan area) will eventually reach 71.3%. Further, model estimation results show that a wide range of socio-demographic factors, travel pattern indicators, technology awareness, and perceptions of AVs are influential in people’s AV adoption timing decision. For instance, frequent long-distance travelers are found to make the adoption decision more innovatively while those who have experienced an accident in their lifetime are found to be more influenced by word of mouth.     

Multi-impact optimization to reduce aviation noise and emissions

This paper describes a multi-indicator assessment and minimization problem focused on aviation “Community” noise. The model explores a combined noise and emission objective for airfreight movements at Luxembourg’s Findel Airport. Community noise is evaluated via four population impact indicators emissions are tabulated from in-flight segments in the proximity of the airport and from the contribution of taxiing. A set of scenarios based on trajectory, aircraft scheduling, fleet composition, operational procedures are implemented in software. An integer programming methodology is used to search for minimal environmental impact. An on/off switch allows for an evaluation of each indicator in the objective. Different impacts on the population are explored in a case study involving the Cargolux Airlines International S.A.     

The impact of fuel availability on demand for alternative-fuel vehicles

We study the impact of fuel availability on demand for alternative-fuel vehicles, using data from a survey of potential car buyers in Germany. The survey was conducted as a computer-assisted personal interview and included a choice experiment involving cars with various fuel types. Applying a standard logit model, we show that alternative fuel availability influences choices positively, but its marginal utility diminishes with supply. Furthermore, we derive consumers’ marginal willingness-to-pay for an expanded service station network. The results suggest that a failure to expand the availability of alternative fuel stations represents a significant barrier to the widespread adoption of alternative-fuel vehicles.     

Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social – influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditional discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption.     

Eliciting preferences for adoption of fully automated vehicles using best-worst analysis

Autonomous mobility is one of the rapidly evolving aspects of smart transportation which carries the potential of reshaping both demand and supply sides of transportation systems. While understanding public opinions about autonomous vehicles (AVs) is a compelling step towards their successful implementation, still little is known about to which extent people will embrace this new technology and how the vehicle features will affect their adoption decision. This study presents a new approach for modeling the adoption behavior of fully AVs using the profile-case best-worst scaling model. In this approach, an AV profile which is characterized in terms of the main vehicle attributes and their associated levels is presented to the decision maker and he/she is asked to select the most and the least attractive attributes. Further, a binary adoption question at the end of the choice task inquires if the respondent is willing to purchase the described AV. Utilizing this method, we can recognize the difference between the intrinsic impacts of the vehicle attributes and the impact of the attribute levels on the adoption decision. Results of the analysis indicate that people are much more sensitive to the purchase price and incentive policies such as taking liability away from the “driver” in case of accidents and provision of exclusive lanes for AVs compared to other factors such as fuel efficiency, safety, or environmental friendliness. Further, it is found that millennials with higher income, those who live in the downtown area, and the majority of people who have experienced an accident in the past have greater interests in adopting this technology.     

Effects of regulation-driven green innovations on short sea shipping's environmental and economic performance

Despite its pivotal role in European trade, today’s short sea shipping (SSS) industry faces the dual challenge of lessening its environmental footprint while improving its economic performance. To reduce the pollution caused by their operations, SSS companies are required to comply with increasingly stringent environmental regulations enacted by global and regional authorities such as the European Union and the International Maritime Organization. However, the companies tend to regard those regulations as imposing an additional burden of cost that compromises their capacity to enhance their economic performance. This paper examines the impact of external institutional driver namely regulatory pressure on the adoption of green innovations in SSS and in turn, the impact of those innovations on the environmental and economic performance of SSS companies. To investigate the hypothesised relationships of those constructs, a structural equation model was developed and tested with data from a survey conducted amongst 101 short sea shipping companies headquartered in Europe. As detailed in the paper, the analysis revealed that regulatory pressure has generated green innovations that have enhanced the environmental and economic performance of European SSS companies and, as a result, led to a win–win situation for all parties involved. The paper discusses what those findings imply for SSS firm managers and policymakers who seek to improve the environmental or economic performance of Europe’s SSS industry.     

Multi-criteria optimization of traffic signals: Mobility,safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.     

A structural analysis of vehicle design responses to Corporate Average Fuel Economy policy

The US Corporate Average Fuel Economy (CAFE) regulations are intended to influence automaker vehicle design and pricing choices. CAFE policy has been in effect for the past three decades, and new legislation has raised standards significantly. We present a structural analysis of automaker responses to generic CAFE policies. We depart from prior CAFE analyses by focusing on vehicle design responses in long-run oligopolistic equilibrium, and we view vehicles as differentiated products, taking demand as a general function of price and product attributes. We find that under general cost, demand, and performance functions, single-product profit maximizing firm responses to CAFE standards follow a distinct pattern: firms ignore CAFE when the standard is low, treat CAFE as a vehicle design constraint for moderate standards, and violate CAFE when the standard is high. Further, the point and extent of first violation depends upon the penalty for violation, and the corresponding vehicle design is independent of further standard increases. Thus, increasing CAFE standards will eventually have no further impact on vehicle design if the penalty for violation is also not increased. We implement a case study by incorporating vehicle physics simulation, vehicle manufacturing and technology cost models, and a mixed logit demand model to examine equilibrium powertrain design and price decisions for a fixed vehicle body. Results indicate that equilibrium vehicle design is not bound by current CAFE standards, and vehicle design decisions are directly determined by market competition and consumer preferences. We find that with increased fuel economy standards, a higher violation penalty than the current stagnant penalty is needed to cause firms to increase their design fuel economy at equilibrium. However, the maximum attainable improvement can be modest even if the penalty is doubled. We also find that firms’ design responses are more sensitive to variation in fuel prices than to CAFE standards, within the examined ranges.     

Do parking standards matter? Evaluating the London parking reform with a matched-pair approach

Minimum parking standards, which require developers to build a certain amount of off-street parking spaces, are increasingly criticized for leading to excess parking supply and automobile travel in recent years. However, due to the difficulty in identifying the policy effect, few empirical studies have directly and accurately estimated the effects of parking standards on parking supply. The present study examines a parking reform in London, UK, where minimum parking standards for residential developments were replaced with maximum standards in the early 2000s. Using planning application records, we match neighboring pre-reform and post-reform developments to estimate the effect of the parking standard switch and further identify the “binding” and “capping” effects of minimum and maximum parking standards. It is found that the parking reform in London has led to a reduction of approximately 0.76 parking spaces per unit in residential development applications, or 49% of the pre-reform level. Minimum parking standards seem to have a larger impact than maximum ones on parking supply that fell more upon inner city developments, while maximum parking standards have more influenced suburban neighborhoods. Market forces have played a major role in the decline of parking supply. The findings provide strong evidence for the market distortion effect of minimum parking standards.     

Electrical characteristics of cold ironing energy supply for berthed ships

The reduction of emissions in harbours is of particular importance due to the proximity to human habitation. Vessels normally run onboard generators, typically using diesel fuel, to provide the service loads while berthed. New and upcoming regulations aim to decrease emissions from shipping, and coupled with increased environmental consciousness of ship owners and harbour operators, shore supply is becoming a more popular and feasible option. Cold ironing provides an alternative locally emission-free solution by having berthed ships plug in to the shore electrical network, such that the onboard electrical energy demand is supplied from land. Electrically, a number of different shore network topologies are possible, providing different infrastructural options of supplying power to multiple berths. This paper examines the electrical characteristics of one such installation and the impact on the shoreside electrical network for an existing port using actual visiting ship power profiles. The paper examines how the cold ironing system influences important electrical network characteristics such as bus voltages and power quality, as well as the potential impact on the rest of the utility distribution system.     

The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study

Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.     

A method to measure the eco-efficiency of diesel locomotive

Brazilian railroads transport over 490 million tons a year using diesel-electric locomotives. These locomotives emit several pollutants into the atmosphere and because of that, the railroads seek to reduce emissions and achieve global emission standards. Thus, it is important to analyze the environmental impact of the use of diesel and alternative fuels to reach environmental standards. This paper makes use of a method based upon the World Business Council for Sustainable Development (WBCSD) metrics to evaluate the locomotives’ eco-efficiency. The method was applied to Estrada de Ferro Vitória a Minas (EFVM). Different scenarios representing the exchange of fuel sources and technologies were developed, tested and analyzed. The impacts were evaluated by seven eco-efficiency performance indicators and compared with United States Environmental Protection Agency (EPA) standards. The results offered cost savings and emissions reduction opportunities.     

铁路隧道风险评估若干问题探讨

文章探讨了当前隧道风险评估中存在的隧道初始和残余风险综合等级判定准则的不统一、施工阶段风险评估中风险因素的缺失及初步设计阶段风险评估"谈虎色变"、施工阶段风险评估"避轻就重"等问题,提出了解决这些问题的对策,即初始和残余风险综合等级就高准则、隧道施工阶段快速适时风险评估和特殊地区隧道初始风险等级合理确定等。     

Advances in consumer electric vehicle adoption research: A review and research agenda

In spite of the purported positive environmental consequences of electrifying the light duty vehicle fleet, the number of electric vehicles (EVs) in use is still insignificant. One reason for the modest adoption figures is that the mass acceptance of EVs to a large extent is reliant on consumers’ perception of EVs. This paper presents a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in EVs, as well as an overview of the theoretical perspectives that have been utilized for understanding consumer intentions and adoption behavior towards EVs. In addition, we identify gaps and limitations in existing research and suggest areas in which future research would be able to contribute.     

Life cycle assessment of bituminous pavements produced at various temperatures in the Belgium context

Bituminous mixture is the premier material for road construction in Belgium. Innovative technologies to improve energy efficiency of pavement constructions are necessary. Warm mix asphalt may provide significant energy savings to the asphalt industry, but the environmental impact of the total life cycle has to be investigated. The use of additives may counteract the reduced environmental impact due to energy savings. This paper presents the results of an environmental impact assessment of four wearing course test sections. Using life cycle assessment, hot mix asphalt is compared to a cold asphalt mix with emulsion and warm mix asphalt with two types of additives: a synthetic zeolite and an organic Fischer–Tropsch wax. Neither hot nor warm mix asphalt could be preferred based on the results of this study, because the additive has a major influence on the environmental results. It was seen that the production of bitumen, the transport and energy in order to generate heat mainly contribute to the total environmental impact. The results from the sensitivity analyses show that the total environmental impact of the life of the pavement can vary significantly based on the choice of the specific data source and service life.     

Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

On the grounds that individuals heavily rely on the information that they receive from their peers when evaluating adoption of a radical innovation, this paper proposes a new approach to forecast long-term adoption of connected autonomous vehicles (CAVs). The concept of resistance is employed to explain why individuals typically tend to defer the adoption of an innovation. We assume that there exists a social network among individuals through which they communicate based on certain frequencies. In addition, individuals can be subject to media advertisement based on certain frequencies. An individual’s perceptions are dynamic and change over time as the individual is exposed to advertisement and communicates with satisfied and dissatisfied adopters. We also explicitly allow willingness-to-pay (WTP) to change as a result of peer-to-peer communication. An individual decides to adopt when (i) there is a need for a new vehicles; (ii) his/her WTP is greater than CAV price; and (iii) his/her overall impression about CAVs reaches a cutoff value. Applicability of the proposed approach is shown using a survey of employees of the University of Memphis. Our results show that the automobile fleet will be near homogenous in about 2050 only if CAV prices decrease at an annual rate of 15% or 20%. We find that a 6-month pre-introduction marketing campaign may have no significant impact on adoption trend. Marketing is shown to ignite CAV diffusion but its effect is capped. CAV market share will be close to 100% only if all adopters are satisfied with their purchases; therefore, the probability that an individual becomes a satisfied adopter plays an important role in the trend of adoption. The effect of the latter probability is more pronounced as time goes by and is also more prominent when CAV price reduces at greater rates. Some caveats may be inserted when considering the study results as the findings are subject to sample bias and data limitations.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

The socio-economics of travel behavior and environmental burdens: A Detroit,Michigan regional context

The transportation industry—particularly light-duty vehicles—is a significant contributor of greenhouse gasses, accounting for about one-third of overall emissions in the U.S. Research to date has studied various factors that impact travel behavior of residents with varying socio-economic characteristics. However, research on the socio-economic characteristics of residents and their impact on environmental burdens within a single urban region, as measured by fuel consumption and vehicular emissions, is recognized as under-represented in the U.S. planning and transportation literature. This study focuses on the Detroit region, Michigan, a unique case study due to the scale of suburbanization and urban decline, yet representative of many mid-western cities. The article explores how socio-economic characteristics impact travel patterns and environmental burdens within six Detroit region neighborhoods. Data on individual travel behavior and personal vehicle characteristics gathered from a mail survey enabled an analysis into how associated environmental burdens varied with socio-economic composition. The analysis explores contributions to environmental burdens between poorer urban and wealthier suburban populations.     

Economic costs and environmental impacts of alternative fuel vehicle fleets in local government: An interim assessment of a voluntary ten-year fleet conversion plan

Vehicle fleets are widely viewed by policy makers as attractive first markets for introduction of alternative fuel technologies. Although, it is essential to understand the environmental benefits and economic challenges involved in fleet conversion, the literature provides little understanding of the implementation issues associated with alternative fuel vehicles. This paper examines the cost effectiveness and environmental impact of the conversion of a 180 plus vehicle fleet to alternative fuel vehicle technologies by a public organization at the mid-point of the project implementation. Using multi-year micro data on fuel usage, operational and capital expenditures, mileage and emissions, the paper examines conversion costs and infrastructure investments required, extent of user adoption, and emissions reductions achieved. Results are discussed in terms of their implications for managerial practice in local government fleet agencies and for future research.