Vehicle Kilometers Traveled Research Articles

Are shared automated vehicles good for public- or private-transport-oriented cities (or neither)?

Simulation studies suggest that Shared Automated Vehicles (SAVs) could reduce the total vehicle kilometres travelled (VKT) thanks to efficiently pooling multiple users in one vehicle. However, mode choice studies indicate that SAVs would attract mostly public transport users, leading to an increase in VKT. This paper is among the first to combine these operational and behavioural expectations and the first to do so analytically. In our theoretical set-up, travellers choose between car, public transport, and SAVs, depending on their individual valuation of private travel and other attributes of each mode. We find that the introduction of SAVs lead to a VKT change in public-transport-oriented cities ranging from a small decrease to a large increase, where the latter is true for plausible parameter settings and hence is a cautionary point for SAV-introduction policies. Conversely, SAVs would attract only few travellers in private-transport-oriented cities and therefore would not significantly impact VKT.

Day-of-Week, Month, and Seasonal Demand Variations: Comparing Flow Estimates Across New Travel Data Sources

Transportation planners and engineers are increasingly interested in incorporating demand variations into travel models. Regression models are used to predict and compare variations in permanent traffic recorder (PTR) counts along Texas highways to vehicle-kilometers traveled (VKT) inferred from INRIX’s probe-vehicle data across days of the year. Results suggest INRIX data do not illuminate month-of-year variations in network use, due to random or unexpected shifts in sampling rates, but significant day-of-week differences are clear in both. Furthermore, INRIX appears to capture much more light-duty-vehicle travel than PTRs on Saturdays, but this may be due to location-based services’ over-counting of vehicles carrying multiple mobile devices and/or PTRs’ highway-site bias.

Autonomous driving parking robot systems for urban environmental benefit evaluation

Many countries are now taking environmental issues seriously and working towards neutral. Therefore, this study aims to reduce carbon emissions by suggesting ways to reduce the use of passenger cars and induce the use of public transportation and walking modes. To this end, a valet parking robot system was designed in a transfer car park outside Seoul. The effect of the system was quantified. By implementing the valet parking robot system in the transfer parking lots, the time to find a parking space and the time to park after finding a parking space are reduced when using existing parking lots. In other words, the valet parking robot system can reduce out-of-vehicle time, and the reduction in travel time can induce a modal shift from existing car users to public transport. Furthermore, by reducing the number of cars in the city center, vehicle kilometer traveled(VKT) will be reduced, and the emission of pollutants that cause air pollution will also be reduced due to less traffic on public roads in the city center. This study analyzes this quantitatively and concludes that the implementation of a valet parking robot system can have a positive environmental impact.

Self-adaptive metaheuristic-based emissions reduction in a collaborative vehicle routing problem

Global climate change-related initiatives such as the 2015 Paris Agreement have highlighted the necessity of sustainable transportation. Nevertheless, the rapid growth of e-commerce has notably escalated vehicle kilometres travelled (VKT) and CO2 emissions within cities, posing a direct challenge to sustainability initiatives. To address these challenges, this study formulates a collaborative multi-depot green vehicle routing problem. This model utilises micro-consolidation centres (MCCs) as shared hubs alongside a microscopic approach linking emission rates to vehicle and route characteristics, in order to assess MCCs' effectiveness in reducing CO2 emissions. Introduced here is an innovative self-adaptive metaheuristic algorithm hybridising intelligent water drops and simulated annealing. This methodology differs from established approaches by incorporating a feedback control system that actively monitors the algorithm's performance and convergence towards the global minimum solution. Through continuous adjustments to algorithm parameters via a feedback loop, this methodology strikes a balance between exploitation and exploration. The algorithm is tested in a context-specific approach, first applying it to the Cordeau benchmark and comparing it with previous state-of-the-arts, followed by a case study comparing the collaborative network to an independent one. This approach achieves 43 % and 25 % reductions in VKT and emissions, respectively, enhancing urban logistics networks' efficiency and sustainability.

Income and fuel price elasticities of car use on micro panel data

We estimate the income and fuel price elasticities of private car vehicle kilometres travelled (VKT) using fixed effects on registry micro panel data covering all Swedish households from 1999 to 2018. Such registry data, covering all individuals and cars in the country, are unique to Nordic countries and are comprehensive enough to allow fine segmentation of the population by both income groups and several municipality types. To address potential endogeneity arising if employees receive a wage compensation for long commutes, we apply the temporal changes in earned income tax credits as an instrumental variable. We find lower income and price elasticities (in absolute value) in the large cities, and larger elasticities in suburbs, other cities and in rural areas. We also find that the elasticities decrease with income, excluding the lowest income quartile, having the lowest elasticities. Specifically, we show theoretically and empirically that because the income elasticity varies considerably along the income distribution, the resulting income elasticity depends heavily on how the estimator assigns weight to different income groups, unless the specification explicitly allows for variation in the impact of income on VKT. Moreover, the impact of an income increase depends on to whom the income increase accrues to. For a uniform income increase, 0.2 is the preferred income elasticity. Our preferred long-run fuel price elasticity is −0.53. The short-run elasticities are lower. These elasticities apply to the full population and not only to car owners or drivers.

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.

Potential effects of automated driving on vehicle travel demand: A comparison of three case cities

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%–25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

Including SDGs in low-carbon transport scenarios: A case of Udaipur

<p>Transportation is fundamental to shaping urban form and quality of life. The transport sector contributes to a quarter of global GHG emissions. It is integral to countries’ Nationally Determined Contributions (NDCs) to mitigate global warming or control warming beyond 2 ℃ or 1.5 ℃ above the pre-industrial level. Climate change mitigation in the transport sector demands a tailored approach for cities of the global south—recently urbanizing with increased dependence on motorization—incorporating social aspects of sustainability. The study examines the delivery of climate change mitigation and Sustainable Development Goals (SDG) in Udaipur’s passenger transport sector through six SDGs. Over and above the business-as-usual scenario, the two scenarios presented are the technology scenario, which recalibrates Udaipur’s available low-carbon mobility plan, and the SDG scenario, which addresses social transformations by applying assumptions derived from the primary survey in the city. The socially sensitive SDG scenario prioritizes the mobility demands of those with low or no mobility. It also enhances mobility by retaining the share of non-motorized transport (NMT), intermediate public transport (IPT), and public transport (PT) and regulating excessive use of private motorized vehicles. However, the SDG scenario causes a 26% increase in the vehicle kilometer traveled (VKT), a 24% increase in CO<sub>2</sub> emissions, and a 29% decrease in other GHG emissions over the Technology Scenario.</p>

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

Revisiting built environment and travel behavior: A natural experiment accounting for residential self-selection

Numerous studies have attempted to quantify the genuine impact of the built environment factors on travel behavior which is not through residential self-selection (RSS). Nevertheless, the interactions among built environment variables have rarely been included in investigations. To address this gap, this study utilizes a natural experiment formed by the unique urban housing context in Beijing, China. It specifically focuses on resettlement housing families, whose place of residence is largely exogenous, effectively mitigating the bias caused by RSS. By using a gradient boosting decision trees model, this study investigates the combined influence of the built environment on household vehicle kilometers traveled (VKT). The results show that the net built environment contributes about 64% to household VKT, and RSS plays a trivial role (less than 2%) in the relationship between the built environment and household VKT in Beijing. However, the built environment variables influence household VKT in different ways for resettlement households and commercial housing households. These results increase our understanding of how the built environment influences travel behavior.

“Downs's Law” under the lens of theory: Roads lower congestion and increase distance traveled

Downs (1962) claimed a “Law”: that expressways lower congestion even though they reach maximum traffic flow. In urban economics since Strotz (1965), road capacity is measured by road width, and congestion as the delay in travel: wider roads lower congestion. Duranton and Turner (2011), in an econometric study, atypically defined congestion, not as delay in travel, but as aggregate vehicle kilometers traveled (VKT) relative to the aggregate length of roads, concluding that roads are unlikely to relieve congestion. We first provide the theory behind “Downs's Law”. Then, in a series of theoretical models, we endogenize rent, income, the value of time, leisure, Marshallian productivity, consumption-linked trips, road costs, spatial detail, and a suburb-to-city expressway competing with existing roads. In each case we prove that adding more road capacity lowers congestion and increases utility in the short run when city population is fixed; and lowers congestion in the long run too despite induced travel or population growth. Aggregate travel cost and VKT rise or fall, depending on how much congestion is lowered, on the cost elasticity of travel demand, on location-based income and substitution effects, and on which roads are widened.

Simulation of the impact of tram operations on road network performances

The City government of Semarang has a plan to build a tram way in Semarang Old City Area as a public transportation mode to accommodate tourism activities. The existence of tram way can impact the performance of existing road network due to being built on existing roads side by side with other vehicles. Street network performances discussed in this paper are on the vehicle-kilometres travelled (VKT), and vehicle-hours travelled (VHT), to determine the aforementioned changes, traffic simulation was done with PTV VISSIM software. Vehicle-kilometres travelled decreased by 12,76%, vehicle-hours travelled increased by 10,54%. To minimize the impacts, two mitigation scenarios are simulated, mitigation 1 which is one-way system and mitigation 2 the combination of one-way system and signal optimizations. In mitigation scenario 1 the value of VKT decreased by 13,12%, VHT decreased by 0,65% compared to existing conditions. In mitigation scenario 2 the value of VKT decreased by 17,54%, VHT decreased by 4,93%.

Ecommerce and environmental justice in metro Seattle

Urban distribution centers (UDCs) are opening at unprecedented rates to meet rising home delivery demand. The trend has raised concerns over the equity and environmental justice implications of ecommerce's negative externalities. However, little research exists connecting UDC location to the concentration of urban freight-derived air pollution among marginalized populations. Using spatial data of Amazon UDCs in metropolitan Seattle, this study quantifies the socio-spatial distribution of home delivery-related commercial vehicle kilometers traveled (VKT), corresponding air pollution, and explanatory factors. Results reveal that racial and income factors are relevant to criteria air pollutant exposure caused by home deliveries, due to tracts with majority people of color being closer in proximity to UDCs and highways. Tracts with majority people of color face the highest median concentration of delivery vehicle activity and emissions despite ordering less packages than white populations. While both cargo van and heavy-duty truck emissions disproportionately affect people of color, the socio-spatial distribution of truck emissions shows higher sensitivity to fluctuations in utilization. Prioritizing environmental mitigation of freight activity further up the urban distribution chain in proximity to UDCs, therefore, would have an outsized impact in minimizing disparities in ecommerce's negative externalities.

Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040

The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions.

Analysis of the Actual Usage and Emission Reduction Potential of Electric Heavy-Duty Trucks: A Case Study of a Steel Plant

In order to understand the driving characteristics of electric heavy-duty trucks in practical application scenarios and promote their usage to replace diesel trucks, this study analyzed the actual usage of electric and diesel heavy-duty trucks in a steel factory based on vehicle-monitoring data and remote online monitoring data and estimated the emission reduction potential of the application of electric trucks by using a mileage-based method and the greenhouse gas emission model. The results showed that the electric heavy-duty trucks in the steel factory mostly operated for over 14 h, with a vehicle kilometers traveled (VKT) of 50–300 km each day, which could meet most of the demands of the transportation of the steel industry. The average daily energy consumption for most trucks falls within the range of 210–230 kWh/100 km, with higher consumption in winter than in summer, which can save approximately 18–26% in operating costs compared with diesel trucks. It is estimated that the usage of these electric heavy-duty trucks can achieve an annual reduction of 115.8 tons of NOx emissions, 0.7 tons of PM emissions, and 18,000 tons of CO2 emissions. To further promote the application of electric heavy-duty trucks in China, several policy suggestions, such as introducing priority road-right policies, promoting vehicle and battery leasing markets, and exempting zero-emission vehicles during heavy pollution days, were proposed.

Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data

This article presents an empirical estimation of the effect of fuel prices on vehicle kilometers traveled (VKT) using a panel dataset of 1560 Swiss households over the period 2018–2021. Elasticities are estimated for different segments of households, based on their socio-demographic and vehicle characteristics, as well as on their driving intensity. Our results, based on fixed-effect, instrumental variable, and quantile regression models, indicate relatively large price elasticities and reveal heterogeneity in price sensitivity. Single-member households appear significantly more responsive to price than multiple-member households. Travel-intensive households do respond to changes in gasoline price, while less intensive drivers do not exhibit statistically significant price elasticities. For household segments who do not react to price, tailored non-price measures would be a useful complement to fuel taxes in order to reduce distance traveled and/or avoid imposing too strong a financial burden.

Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data

The rapid growth in data collection, storage, and transformation technologies offered new approaches that can be effectively utilized to improve traffic crash prediction. Considering the probability of traffic crash occurrence vary due to the spatiotemporal heterogeneity, this study proposes a state-of-the-art deep learning-based model that incorporates spatiotemporal information for the short-term crash prediction, named as Deep Spatiotemporal Hybrid Network (DSHN). The model integrates Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Artificial Neural Network (ANN) to incorporate the synergistic power of individual models. The study utilizes different data sources such as big traffic data collected from Paris road network sensors, weather conditions, infrastructure, holidays, and crash data. The results indicated that the proposed DSHN model outperforms the baseline models with an Area Under Curve (AUC) of about 0.800, an accuracy of 0.757, and a false alarm rate of 0.217. In addition, the importance of each data type is evaluated to investigate their impacts on the prediction performance of models. The sensitivity analysis results indicate that the road sensor data that includes average speed, vehicle kilometer traveled (VKT), and weighted average occupancy has the highest impact on the prediction accuracy.

Optimization of Shared Autonomous Vehicles Routing Problem: From the View of Parking

Shared autonomous vehicles (SAVs) have great potential for achieving beneficial changes to the society. Although recent studies have explored the traffic safety, economic benefits and environmental impact, parking decisions of SAVs is rarely considered. SAVs need to park to avoid cruising during the interval between services. Appropriate parking decisions can contribute to reduce the vehicle kilometers traveled (VKT). This study synergistically considers routing and parking of SAVs for system optimization. Since the problem is NP-hard, we develop a variable neighborhood search (VNS) heuristic to solve it. The heuristic aims to minimize the VKT, the number of SAVs and the parking cost by systematic changes of neighborhood. A series of experiments based on the Anaheim network prove the high solving efficiency and quality of the heuristic. Results also indicate that the marginal cost of the system decreases with the increase in travel demand and the VKT increases with the increase in parking fees.

A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions, COVID-19 impact, and cargo bikes

The e-commerce industry has experienced significant growth in the past decade, particularly post-COVID. To accommodate such growth, the parcel delivery sector has also grown rapidly. However, there is a lack of study that properly evaluates its social and environmental impacts at a large scale. A model is proposed to analyze such impacts. A parcel generation process is presented to convert public data into parcel volumes and stops. A continuous approximation model is fitted to estimate the length of parcel service tours. A case study is conducted using New York City (NYC) data. The parcel generation is shown to be a valid fit. The continuous approximation model parameters have R2 values of 98% or higher. The model output is validated againstUPS truck trips. Application of the model to2021 suggestsresidential parcel deliveries contributed to 0.05% of total daily vehicle-kilometer-traveled (VKT) in NYCcorrespondingto 14.4 metric tons of carbon equivalent (MTCE) emissions per day. COVID-19 contributed to an increase in parcel deliveries that led to up to 1 064.3 MTCE of annual greenhouse gas (GHG) emissions in NYC (which could power 532 standard US households for ayear). The existing bike lane infrastructure can support the substitution of 17% of parcel deliveries by cargo bikes, which would reduce VKT by 11%. Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5% up to 30%. If 28 km of additional bike lanes are made, parcel delivery substitution citywide could increase from 17% to 34% via cargo bike and save an additional 2.3 MTCE per day. Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem, Sunset Park, and Bushwick.