What encourages people to carpool? An evaluation of factors with meta-analysis

Non-household carpools (where two or more commuters from different residences travel together in the same private vehicle) bring public benefits. To encourage and incentivise it, transport practitioners and researchers must understand its private motivations and deterrents. Existing studies often report conflicting results or non-generalisable findings. Thus, a quantitative systematic review of the literature body is needed. Using meta-analysis, this study synthesised 22 existing empirical studies (representing over 79,000 observations) to produce an integrated review of the carpooling literature. The meta-analysis determined 24 non-household carpooling factors, and their effect sizes. Factors such as number of employees (\(\bar{r} = 0.42\)), partner matching programs (\(\bar{r} = 0.42\)), female (\(\bar{r} = 0.22\)) and fixed work schedule (\(\bar{r} = 0.15\)) were found to have strong effects on carpooling while judgmental factors (such as the motivation to save costs) only exhibited small influence (\(\bar{r} < 0.1\)). Based on the significant effects, the paper discussed prospects for improving carpooling uptake by developing: (i) target demographics, (ii) selling points for marketing, (iii) carpooling partner programs and (iv) multiple employer ‘super-pools’. The results warrant caution due to the small amount of studies synthesised. Transport practitioners might plan carpooling policies based on the findings; and transportation researchers might use the list of factors to model carpooling behaviour.     

Help or hindrance? The travel,energy and carbon impacts of highly automated vehicles

Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.     

What is the market potential of plug-in electric vehicles as commercial passenger cars? A case study from Germany

Commercial passenger cars are a possible early market segment for plug-in electric vehicles (PEVs). Compared to privately owned vehicles, the commercial vehicle segment is characterized by higher mileage and a higher share of vehicle sales in Germany. To this point, there are only few studies which analyze the commercial passenger car sector and arrive at contradictory results due to insufficient driving profile data with an observation period of only one day. Here, we calculate the market potential of PEVs for the German commercial passenger car sector by determining the technical and economical potential for PEVs in 2020 from multi-day driving profiles. We find that commercial vehicles are better suited for PEVs than private ones since they show higher average annual mileage and drive more regularly. About 87% of the analyzed three-week vehicle profiles can technically be fulfilled by battery electric vehicles (BEVs) with an electric driving range of about 110 km while plug-in hybrid electric vehicles (PHEVs) with an electric range of 40 km could obtain an electric driving share of 60% on average. In moderate energy price scenarios, PEVs can reach a market share of 2–4% in the German commercial passenger car sales by 2020 and especially the large commercial branches (Trade, Manufacturing, Administrative services and Other services) are important. However, our analysis shows a high sensitivity of results to energy and battery prices as well as electric consumptions.     

What are the determinants in making people free riders in proof-of-payment transit systems? Evidence from Italy

Nowadays, in proof-of-payment transit systems, fare evasion is provoking strong interest in public transport companies (PTCs) due to the relevant economic losses, social inequity and increased levels of violence affecting personal security. Therefore, there is the need to recognize possible fare evaders. By using 2177 on-board personal interviews, gathered from an Italian PTC, and logistic regression models, we isolate determinants of possible free-rider passengers and, hence, those whom it might be advisable to target in order to capitalize on the effect of the application of countermeasures on fare evasion put forward by the local PTC. We show that males, younger than 26 years, with a low education level, unemployed and/or students and without an alternative mode of transport other than the bus are more probably fare evaders. Moreover, people who make trips of shorter than 15 min, who are systematic users and are not satisfied with the service are possible fare evaders. Finally, we found that a low level of inspection, knowledge of fines and previous ticket violations are determinants which make people more prone to evade fares. These outcomes are very useful, because, to the best of our knowledge, they represent the first empirical contribution showing the determinants which help evaluate the propensity to be a fare evader, in probabilistic terms. Moreover, they could help PTCs understand who might be a fare evader, in order to anticipate suitable countermeasures.     

What do people know about their public transport options?

This paper studies the memory representations of residents regarding the public transport system in their city. Telephone interviews were conducted with a representative sample of 204 inhabitants in a selected residential inner-city area in Stockholm. Route knowledge questions, recognition tasks, free-recall tasks and estimations of service frequency were used to explore memory representations. The results showed that, in general, residents in metropolitan areas have good knowledge of the public transport options along well-known transport corridors. The memory representation of lesser-known transport corridors tends to be of a poorer quality. In the results presented here, the variables gender, age, employment status, level of education and car availability had no correlation with the quality of the memory representation, but experience increased knowledge. Although frequent users of public transport had a more detailed representation of the system, the less frequent users also had a considerable- and good-memory representation. An explorative hierarchy for representation of public transport lines in the memory is proposed. It is hypothesised that memory representations of a transport line can be affected by the following three factors: the extent to which a line is visible in the urban area, the straightness of the routes and whether or not stops are labelled, for example, by destination area. Simply put, these factors determine how well a person knows a line. It was found that people first remember a commuter train and a trunk bus line, followed by metro lines and suburban buses and finally normal inner-city buses with the poorest anchorage in memory.

Public charging infrastructure for plug-in electric vehicles: What is it worth?

Lack of charging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Public charging infrastructure has tangible and intangible value, such as reducing range anxiety or building confidence in the future of the PEV market. Quantifying the value of public charging infrastructure can inform analysis of investment decisions and can help predict the impact of charging infrastructure on future PEV sales. Estimates of willingness to pay (WTP) based on stated preference surveys are limited by consumers’ lack of familiarity with PEVs. As an alternative, we focus on quantifying the tangible value of public PEV chargers in terms of their ability to displace gasoline use for PHEVs and to enable additional electric (e−) vehicle miles for BEVs, thereby mitigating the limitations of shorter range and longer recharging time. Simulation studies provide data that can be used to quantify e-miles enabled by public chargers and the value of additional e-miles can be inferred from econometric estimates of WTP for increased vehicle range. Functions are synthesized that estimate the WTP for public charging infrastructure by plug-in hybrid and battery electric vehicles, conditional on vehicle range, annual vehicle travel, pre-existing charging infrastructure, energy prices, vehicle efficiency, and household income. A case study based on California’s public charging network in 2017 indicates that, to the purchaser of a new BEV with a 100-mile range and home recharging, existing public fast chargers are worth about $1500 for intraregional travel, and fast chargers along intercity routes are valued at over $6500.     

What drives the gap? Applying the Blinder–Oaxaca decomposition method to examine generational differences in transportation-related attitudes

Transportation - Considerable recent work suggests that Millennials’ behaviors may be converging with those of Generation X as they enter later life stages, but few have investigated whether...     

Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia

Many studies have begun investigating possible transportation landscapes in the autonomous vehicle (AV) era, but empirical results on longer-term decisions are limited. We address this gap using data collected from a survey designed and implemented for Georgia residents in 2017–2018. Focusing on a hypothetical all-AV future, this section of the survey included questions regarding advantages/disadvantages of AVs, short-term mode choice impacts, medium-term impacts on activity patterns, and long-term behavioral changes – specifically, whether/how AVs will influence individuals to change residential location and the number of cars in the household. We hypothesize that AVs could act in concert with attitudinal preferences to stimulate changes in these long-term decisions, and that some medium-term activity changes triggered by AVs could motivate people to relocate their residence or shed household vehicles. We applied exploratory factor analysis to measure the perceived likelihood that AVs would prompt various medium-term changes. We then included some of those measures, among other variables, in a cross-nested logit (CNL) model of the choice of the residential location/vehicle ownership bundle. Although more than half of respondents expected “no change” in their bundle, we found that younger, lower income, pro-suburban, and pro-non-car-mode individuals were more likely to anticipate changing their selections. In addition, some expected medium-term impacts of AVs influenced changes in these longer-term choices. We further applied the CNL model to two population segments (Atlanta and non-Atlanta-region residents). We found notable improvement in goodness of fit and different effects of factors across segments, signifying the existence of geography-related taste heterogeneity.     

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.     

Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles

Transportation - It is widely believed that vehicle automation will change how travelers perceive the value of travel time (VoTT), but the magnitude of this effect is still unknown. This study...     

Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment

We study the trip scheduling preferences of train commuters in a real-life setting. The underlying data have been collected during large-scale peak avoidance experiment conducted in the Netherlands, in which participants could earn monetary rewards for traveling outside peak hours. The experiment included ca. 1000 participants and lasted for multiple months. Holders of an annual train pass were invited to join the experiment, and a customized smartphone app was used to measure the travel behavior of the participants. We find that compared to the pre-measurement, the relative share of peak trips decreased by 22% during the reward period, and by 10% during the post-measurement. By combining multiple complementary data sources, we are able to specify and estimate (MNL and panel latent class) departure time choice models. These yield plausible estimates for the monetary values that participants attach to reducing travel time, schedule delays, the number of transfers, crowdedness, and unreliability.     

No longer in the driver’s seat: How do affective motivations impact consumer interest in automated vehicles?

Transportation - Emerging advancements in automated vehicles may dramatically change how transportation system users engage cars as symbols and tools to exert control over their lives, time use,...     

Do social norms regarding carbon offsetting affect individual preferences towards this policy? Results from a stated choice experiment

This study investigates the idea that people’s willingness to offset flight-related carbon emissions is a function of the collective participation rate, which can be regarded as a social norm, towards carbon offsetting. Additionally, we reveal people’s preferences toward two other environmental policies; a baggage allowance and airline eco-efficiency index. A discrete choice experiment is designed and administrated among a sample of air travelers. The results indicate that carbon offsetting generates utility, with people gaining more utility when the collective participation rate is high. Additionally, it was found that the baggage allowance and the eco-efficiency index strongly influenced respondents’ airline choices. People also became more sensitivity towards a baggage allowance and the eco-efficiency label, when the collective offsetting rate was high.     

What drives the drivers? Predicting turnover intentions in the Belgian bus and coach industry

The bus industry is characterized by demanding jobs and high turnover rates. In this study we gather essential insights that can help companies and industry-level policy makers increase the attractiveness of the profession and design effective retention policies. We compare the factors that induce Belgian drivers to leave their current organization with those inducing them to leave the industry. Key factors increasing the likelihood to consider quitting the company are a negative work-life balance, a lack of social support and a temporary contract. Dominant factors to consider quitting the bus driver profession are a lack of fulfillment, a demanding job environment and a negative work-life balance.     

Can regional transportation and land-use planning achieve deep reductions in GHG emissions from vehicles?

The Intergovernmental Panel on Climate Change estimates that greenhouse gas emissions (GHG) must be cut 40–70% by 2050 to prevent a greater than 2 °Celsius increase in the global mean temperature; a threshold that may avoid the most severe climate change impacts. Transportation accounts for about one third of GHG emissions in the United States; reducing these emissions should therefore be an important part of any strategy aimed at meeting the IPCC targets. Prior studies find that improvements in vehicle energy efficiency or decarbonization of the transportation fuel supply would be required for the transportation sector to achieve the IPCC targets. Strategies that could be implemented by regional transportation planning organizations are generally found to have only a modest GHG reduction potential. In this study we challenge these findings. We evaluate what it would take to achieve deep GHG emission reductions from transportation without advances in vehicle energy efficiency and fuel decarbonization beyond what is currently expected under existing regulations and market expectations. We find, based on modeling conducted in the Albuquerque, New Mexico metropolitan area that it is possible to achieve deep reductions that may be able to achieve the IPCC targets. Achieving deep reductions requires changes in transportation policy and land-use planning that go far beyond what is currently planned in Albuquerque and likely anywhere else in the United States.     

How will we fund our roads? A case of decreasing revenue from electric vehicles

Annual expenditures for transportation infrastructure have recently surpassed the funding available through tax and fee collection. One large source of revenue generation for transportation infrastructure is use fees that are charged through taxes on gasoline both on a federal and state level. A massive adoption of electric vehicles (EVs) in the United States would result in significantly lower gasoline consumption and thus reduce the revenue collected to maintain the U.S. transportation infrastructure. We investigate how different vehicles will change the annual fee collected on a marginal basis. In addition, we assess the effects of adoption of alternative vehicles on revenues using several projections of alternative vehicles adoption, both on a state-by-state basis and at the national level. We find that baseline midsize and compact vehicles such as the Toyota Camry and Honda Civic generate approximately $2500–$4000 in tax revenue over their lifetime. Under the current funding structure, battery-electric vehicles (BEVs) such as the Nissan Leaf generate substantially less at $400–$1300, while plug-in hybrid electric vehicles (PHEVs) such as the Chevrolet Volt generate $1500–$2700. Even in states with high lifetime fees due to fuel taxes, such as California, revenue generation can be upwards of 50% lower than in states with high registration fees such as Colorado. Total annual revenue generation decreases by about $200 million by 2025 as a result of EV adoption in our base case, but in projections with larger adoption of alternative vehicles could lead to revenue generation reductions as large as $900 million by 2025. Potential schemes that charge user fees on alternative fuel vehicles to overcome the decrease in revenue include a flat annual registration fee at 0.6% of the vehicle’s manufacturer suggested retail price (MSRP) or 2

per mile fee.     

Serving vs. settling: What drives the establishment of low-cost carriers’ foreign bases?

This paper uses a probit model on a cross-sectional dataset of 202 airports and 29 airlines to assess the drivers for the establishment of foreign bases by European low-cost carriers (LCCs). We find that managerial, organizational, and environmental factors impact on foreign base establishment. In particular, there is evidence that the presence of a growth-oriented firm leader and the strategic importance of an airport for an airline significantly increase the probability of the presence of bases, while national unit labor costs, the volatility of flight operations and the membership of an LCC in an airline group significantly decrease this probability.     

What drives freight transportation customer loyalty? Diverging marketing approaches for the air freight express industry

Transportation - Going beyond the traditional business to consumer air transport passenger perspective, this paper examines what we can learn from combining transaction marketing (TM) and...     

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.     

Electric vehicles: How much range is required for a day’s driving?

One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day’s range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100 miles in one day, and 21% never exceeded 150 miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2 days a year, the same 100 mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today’s limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5 pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested.