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A Trip-Chain-based approach to generate travel demands for shared autonomous vehicle systems modeling
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Ridesharing is a shared mobility service in which passengers and drivers with similar origins and destinations are matched to travel in the same vehicle. This service utilises unused seats in vehicles and multi-passenger rides to reduce the cost of travel. To promote ridesharing, both service providers and policymakers should carefully analyse passenger adoption behaviour to support future decision-making and planning. In this paper, 80 studies on passenger ridesharing behaviour published since 2004 are reviewed. The motivating factors and barriers are analysed and classified in terms of demographic factors, psychological factors, and situational factors, and boundary conditions are included. The work provides a corresponding research framework on ridesharing behaviour. Finally, the current literature gaps are summarised and research recommendations are provided. This study provides a comprehensive and systematic research basis for ridesharing studies, and presents important theoretical and practical contributions to guide sustainable ridesharing behaviour.
Advancement in the fields of electrification, automation, and digitalisation and emerging social trends are fuelling the transformation of road transport resulting in the introduction of various innovative mobility solutions. Yet the reaction of people to many of the new solutions is still vastly unknown. This creates an unprecedented quandary for transport planners who are requested to design future transport systems and create the related investment plans without fully validated models to base the assessment upon. As some evidence on citizens’ behaviour concerning new mobility solutions starts to be progressively made available, first attempts to update the existing models begin to emerge. Nevertheless, a lot more is needed as some of the transpiring mobility solutions have not yet reached the market, making the corresponding behaviour changes imponderable. In this context, the main purpose of this paper is to provide a review on how travel behaviour changes linked to the deployment of new mobility solutions have been considered in travel demand models. The new mobility solutions studied include carsharing, dynamic ridesharing, micromobility sharing services, and personal and shared autonomous vehicles. An overview and comparison of relevant studies implementing activity or trip-based demand models and other methodologies are presented. The analysis shows that the results of the different studies heavily depend on the extent to which behavioural changes are considered. The results of the review thus point to the need for holistic demand models that carefully mimic the urban reality with everything it has to offer and account for the importance of individual traits in the decision-making processes. Such models need an in-depth understanding of the microscopic mechanisms leading to the travel behaviour shifts linked to the most innovative mobility solutions. To achieve this level of detail, mobility living labs and their real-life experiments and experience with citizens, which are flourishing in Europe, are suggested to play a crucial role in the years to come.
The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
Over the last few years, a large emphasis has been devoted to Autonomous Vehicles (AVs), as vehicle automation promises a large number of benefits such as: improving mobility and minimization of energy and emissions. Additionally, AVs represent a major tool in the fight against pandemics as autonomous vehicles can be used to transport people while maintaining isolation and sterilization. Thus, manufacturers are racing to introduce AVs as fast as possible. However, laws and regulations are not yet ready for this change and the legal sector is following the development of autonomous vehicles instead of taking the lead. This paper provides a comprehensive review of the previous studies in the transportation field that involve AVs with the aim of exploring the implications of AVs on the safety, public behavior, land use, economy, society and environment, public health, and benefits of autonomous vehicles in fighting pandemics.
Future transport systems are expected to rely to a much greater extent on intelligent ride-sharing services, thus ride-sharing will have a significant impact on traffic planning and demand forecasting. This paper aims to review the classic transport planning four-stage model critically and extend it to make it more appropriate for traffic planning in the context of the rise of ride-sharing.
The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.
The development of production automobiles involved vehicles that were propelled by internal combustion engines. Improvements in the internal combustion technology continue to take place even today. However, electric vehicles are slowly making way into the bigger picture. Development in autonomous vehicle technology is also gathering pace. Although there has been remarkable progress in this domain, much needs to be accomplished. Adoption of autonomous vehicle technology has multiple benefits. Autonomous car companies have spent large amount of resources on the development of autonomous vehicle technology, with an aim to fully commercialize the technology. Several issues cause hindrance to the achievement of this goal. These issues comprise technical, non-technical and legal challenges. The future of the technology is assuring and ambitious, however, the challenges must be overcome.
Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.
The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.
Mobility-on-demand systems consisting of shared autonomous vehicles (SAVs) are expected to improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, several issues in their implementation remain open, such as unifying the vehicle and ride-sharing assignment with rebalancing non-occupied vehicles. Furthermore, proposed SAV systems are evaluated in isolation from other traffic; no congestion is taken into account when assigning requests or calculating routes. To address this gap, we present Shared Autonomous Mobility-on-Demand system (SAMoD), a reinforcement learning-based approach to vehicle relocation and ride-sharing request assignment. Each vehicle learns its pickup and rebalancing behaviour based on local current and observed historical demand. We evaluate SAMoD on Manhattan network using NYC taxi data in microsimulator SUMO. We investigate SAMoD performance in the presence of congestion generated by private vehicles, as well as investigate impact of different percentages of SAMoD vehicles in the system on overall traffic network performance.
The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.
Rapid advances in technologies have accelerated the timeline for public use of fully-automated and communications-connected vehicles. Public opinion on self-driving vehicles or AVs is evolving rapidly, and many behavioral questions have not yet been addressed. This study emphasizes AV mode choices, including Americans’ willingness to pay (WTP) to ride with a stranger in a shared AV fleet vehicle on various trip types and the long-distance travel impacts of AVs. Exactly 2,588 complete responses to a stated-preference survey with 70 questions provide valuable insights on privacy concerns, safety and dynamic ride-sharing with strangers, long-distance travel and preferences for smarter vehicles and transport systems. Two hurdle models (which allow for a high share of zero-value responses) were estimated: one to predict WTP to share a ride and another to determine WTP to anonymize location while using AVs, and a multinomial logit was developed to estimate long-distance mode choices with AVs and SAVs available. Results suggest that WTP to share rides will rise over time, for a variety of reasons, and SAV use will be particularly popular for long-distance business travel. Elasticity estimates suggest that privacy may not be an important concern for AV-based travel.
Shared transportation is playing an increasingly important role in sustainable urban transportation planning and control. Because it significantly affects people’s daily life, socio-economic development, and the environment, shared transportation has attracted attention from scholars and practitioners alike. For the former, the large number of articles published on the topic reveals the growing interest. Of interest are the articles that focus on travel behaviors, user characteristics, and social-economic impacts of shared transportation. Herein, we review 356 peer-reviewed articles on the topic that were published between January 2003 and September 2017. We employ a bibliometric method to investigate the overall characteristics, research methodology, research highlights, and research areas of these articles. Our analysis explores and discusses user travel behaviors, traffic satisfaction, key determinants, impact, development planning, and policies. Finally, we provide a detailed discussion on the future research challenges and new research directions for shared transportation.
Innovations in the mobility industry such as automated and connected cars could significantly reduce congestion and emissions by allowing the traffic to flow more freely and reducing the number of vehicles according to some researchers. However, the effectiveness of these sustainable product and service innovations is often limited by unexpected changes in consumption: some researchers thus hypothesize that the higher comfort and improved quality of time in driverless cars could lead to an increase in demand for driving with autonomous vehicles. So far, there is a lack of empirical evidence supporting either one or other of these hypotheses. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as indicators for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 participants in Germany. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether conventional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, the findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more emphasis should be placed in making public transport more attractive if sustainable mobility is to be developed.
Shared autonomous vehicles are considered to have a transformative impact on future urban transportation and especially Shared Mobility. In order to assess the transport-related impact of this new mode, various models and simulations are under development. The majority of recently proposed simulations are based on activity-based multi-agent approaches. Thanks to the disaggregated level of data in multi-agent simulation, the traveler decision making mechanisms might be individualized according the attributes. In this paper we try to take in advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles. To illustrate the proposed methodology the transport system of Paris is simulated by using an activity-based multi-agent tool called MATSim. The traveler preferences toward shared autonomous vehicles use are also summarized based on the literature review.
Information and communication technologies have opened the way to new solutions for urban mobility that provide better ways to match individuals with on-demand vehicles. However, a fundamental unsolved problem is how best to size and operate a fleet of vehicles, given a certain demand for personal mobility. Previous studies¹⁻⁵ either do not provide a scalable solution or require changes in human attitudes towards mobility. Here we provide a network-based solution to the following 'minimum fleet problem', given a collection of trips (specified by origin, destination and start time), of how to determine the minimum number of vehicles needed to serve all the trips without incurring any delay to the passengers. By introducing the notion of a 'vehicle-sharing network', we present an optimal computationally efficient solution to the problem, as well as a nearly optimal solution amenable to real-time implementation. We test both solutions on a dataset of 150 million taxi trips taken in the city of New York over one year⁶. The real-time implementation of the method with near-optimal service levels allows a 30 per cent reduction in fleet size compared to current taxi operation. Although constraints on driver availability and the existence of abnormal trip demands may lead to a relatively larger optimal value for the fleet size than that predicted here, the fleet size remains robust for a wide range of variations in historical trip demand. These predicted reductions in fleet size follow directly from a reorganization of taxi dispatching that could be implemented with a simple urban app; they do not assume ride sharing⁷⁻⁹, nor require changes to regulations, business models, or human attitudes towards mobility to become effective. Our results could become even more relevant in the years ahead as fleets of networked, self-driving cars become commonplace¹⁰⁻¹⁴.
Contemporary systems of mobility are undergoing a transition towards automation. In the UK, this transition is being led by (often new) partnerships between incumbent manufacturers and new entrants, in collaboration with national governments, local/regional councils, and research institutions. This paper first offers a framework for analyzing the governance of the transition, adapting ideas from the Transition Management (TM) perspective, and then applies the framework to ongoing automated vehicle transition dynamics in the UK. The empirical analysis suggests that the UK has adopted a reasonably comprehensive approach to the governing of automated vehicle innovation but that this approach cannot be characterized as sufficiently inclusive, democratic, diverse and open. The lack of inclusivity, democracy, diversity and openness is symptomatic of the post-political character of how the UK’s automated mobility transition is being governed. The paper ends with a call for a reconfiguration of the automated vehicle transition in the UK and beyond, so that much more space is created for dissent and for reflexive and comprehensive big picture thinking on (automated) mobility futures.
With 36 ventures testing autonomous vehicles (AVs) in the State of California, commercial deployment of this disruptive technology is almost around the corner (California, 2017). Different business models of AVs, including Shared AVs (SAVs) and Private AVs (PAVs), will lead to significantly different changes in regional vehicle inventory and Vehicle Miles Travelled (VMT). Most prior studies have already explored the impact of SAVs on vehicle ownership and VMT generation. Limited understanding has been gained regarding vehicle ownership reduction and unoccupied VMT generation potentials in the era of PAVs. Motivated by such research gap, this study develops models to examine how much vehicle ownership reduction can be achieved once private conventional vehicles are replaced by AVs and the spatial distribution of unoccupied VMT accompanied with the vehicle reduction. The models are implemented using travel survey and synthesized trip profile from Atlanta Metropolitan Area. The results show that more than 18% of the households can reduce vehicles, while maintaining the current travel patterns. This can be translated into a 9.5% reduction in private vehicles in the study region. Meanwhile, 29.8 unoccupied VMT will be induced per day per reduced vehicles. A majority of the unoccupied VMT will be loaded on interstate highways and expressways and the largest percentage inflation in VMT will occur on minor local roads. The results can provide implications for evolving trends in household vehicles uses and the location of dedicated AV lanes in the PAV dominated future.
Autonomous vehicles will have tremendous impact on our cities and regions. This rapidly emerging technology will affect the transport system in its entirety including changes in energy consumption; increased safety, climate change impacts, efficiency of transport operations and the platooning of trucks carrying freight. Primary questions remain. What are reasonable expectations of the impact of autonomous and connected vehicles on travel demand, energy consumption, and emissions? Can vehicle to vehicle communication have a significant impact on congestion and vehicle movements that will result in smoother traffic flow and accompanying reductions in energy and emissions? What policies and regulations guiding the operation of autonomous and connected vehicles will be enacted and to what extent will autonomous vehicles penetrate the market over what time period? This article attempts to identify and quantify the impact of autonomous vehicles on energy through development of scenarios that gauge the potential range and contribution of this emerging technology. The scenarios reflect an assessment of the state of practice and current research conducted in both the public and the private sector. Three scenarios are examined in details including energy impacts based on partial or full automation and personal versus a shared vehicle future for autonomous vehicles. There are numerous possible scenarios that may unfold but each will have to be responsive to our multimodal transport system with an objective to optimize modal, technological, financial and energy resources now and in the future.
The travel forecasting process is at the heart of urban transportation planning. Travel forecasting models are used to project future traffic and are the basis for the determination of the need for new road capacity, transit service changes and changes in land use policies and patterns. Travel demand modeling involves a series of mathematical models that attempt to simulate human behavior while traveling. The models are done in a sequence of steps that answer a series of questions about traveler decisions. Attempts are made to simulate all choices that travelers make in response to a given system of highways, transit and policies. Many assumptions need to be made about how people make decisions, the factors they consider and how they react in a particular transportation alternative. The travel simulation process follows trips as they begin at a trip generation zone, move through a network of links and nodes and end at a trip attracting zone. The simulation process is known as the four step process for the four basic models used. These are: trip generation, trip distribution, modal split and traffic assignments. This paper describes the process of the traditional four steps transportation modeling system using a simplified transport network in the context of Dhaka City, Bangladesh.
Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upper-class backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone.
Introduction
Travel behaviour research involves the study of both the economic and behavioural (or sociological) aspects of travel. Numerous factors influence the demand for travel in complex ways, resulting in intricate causal relationships which require the use of a wide range of data analysis methodologies and interpretations. This study provides an overview of travel behaviour research conducted in developing countries in order to gain insight into the factors that influence travel demand and the methodologies used to investigate them.
Methods
Four multi-subject electronic databases were searched, and the inclusion criteria adopted was in line with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Twenty studies met our inclusion criteria.
Results
The main methods of analysis used in the studies reviewed are rooted in multivariate regression models and structural equation modeling with the most predominant methods being SEM and the MNL. The studies reviewed show that age, gender, income, work status, education level, family size, cost, and availability of travel modes are the most important factors influencing travel demand in developing cities.
Conclusion
The findings serve as a resource for scholars interested in this subject, as they provide insights that may be used to perform better travel demand studies. There is need for the development of a general modeling framework and guidelines for selecting appropriate models for use in travel behaviour research.
Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.
This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.
This study micro-simulates 2% and 5% of the region's 9.5 million daily person-trips and 20% of trips in the central Twin Cities with shared autonomous vehicles (SAVs) in the 7-county Minneapolis-Saint Paul region using MATSim to appreciate the effects of different trip-making densities and curb-use restrictions. Results suggest the average SAV in this region can serve at most 30 person-trips per day with less than 5 minutes average wait time, but generating 13% more vehicle-miles traveled (VMT). With dynamic ride-sharing (DRS), SAV VMT fell, on average, by 17% and empty VMT (eVMT) fell by 26%. Compared to idling-at-curb scenarios, parking-restricted scenarios generated 8% more VMT. Relying on 52 mi/gallon hybrid electric SAVs, as opposed to a 31 mi/gallon conventional drivetrain SAV, is estimated to lower travelers' energy use by 21% and reduce tailpipe emissions by 30%, assuming no new or longer trips. Similarly, a 106 mi/gallon equivalent battery-electric fleet does much better by lowering energy use by 64%.
The advent of autonomous vehicle technologies and the emergence of new ride-sourcing business models has spurred interest in Automated Mobility-on-Demand (AMOD) as a prospective solution to meet the challenges of urbanization. AMOD has the potential of providing a convenient, reliable and affordable mobility service through more competitive cost structures enabled by autonomy (relative to existing services) and more efficient centralized fleet operations. However, the short and medium-term impacts of AMOD are as yet uncertain. On the one hand, it has the potential to alleviate congestion through increased ride-sharing and reduced car-ownership, and by complementing mass-transit. Conversely, AMOD may in fact worsen congestion due to induced demand, the cannibalization of public transit shares, and an increase in Vehicle-Kilometers Traveled (VKT) because of rebalancing and empty trips. This study attempts to systematically examine the impacts of AMOD on transportation in Singapore through agent-based simulation, modeling demand, supply and their interactions explicitly. On the demand side, we utilize an activity-based model system, that draws on data from a smartphone-based stated preferences survey conducted in Singapore. On the supply side, we model the operations of the AMOD fleet (including the assignment of requests to vehicles and rebalancing), which are integrated within a multimodal mesoscopic traffic simulator. Comprehensive simulations are conducted using a model of Singapore for the year 2030 and yield insights into the impacts of AMOD in dense transit-dependent cities from the perspective of the transportation planner, fleet operator, and user. The findings suggest that an unregulated introduction of AMOD can cause significant increases in network congestion and VKT, and have important policy implications that could potentially inform future deployments of AMOD.
Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.
Carpooling rates in America have been falling for decades. But new technologies may offer solutions to traditional carpooling barriers and usher in a new chapter in shared car travel. Ride-hail services connect riders to drivers through smartphone applications. The largest ride-hail companies, Uber and Lyft, offer shared carpool (rideshare) services to connect riders traveling in the same directions and at the same times. Although researchers have recently begun to understand who uses ride-hail services, few have yet investigated ridesharing. To fill this gap, I ask and answer two questions. First, what factors are associated with where ridesharing occurs? Second, what factors are associated with who rideshares? To answer both questions, I use trip-level data of 6.3 million Lyft trips, including 1.9 million Lyft Shared trips, taken in Los Angeles County in 2016. Findings reveal that while about one-third of Lyft trips are on Lyft Shared, these rideshare trips are made by a small fraction of ride-hail users. Just one-third of ride-hail users made even one rideshare trip over the three-month study period, and just ten percent of all Lyft riders made 94 percent of rideshare trips. People living in dense and lower-income neighborhoods share a higher proportion of ride-hail trips compared to riders living in other neighborhoods. Less ridesharing occurs in racial and ethnically diverse neighborhoods compared to neighborhoods where clear racial or ethnic majorities exist. Cities seeking to increase sharing in ride-hail services should focus efforts on attracting non-users, including pricing to encourage shared rather than solo car trips. Implemented policies should avoid undercutting demand for transit or active travel, which remain the most efficient modes on our streets.
Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.
Rapid advances in the development of autonomous and alternative-fuel vehicles (AFVs) are likely to transform the future of mobility and could bring benefits such as improved road safety and lower emissions. Achieving these potential benefits requires widespread consumer support for these disruptive technologies. To date, research to explore consumer perceptions of transport innovations has tended to consider them in isolation (e.g., driverless cars, electric vehicles). The current paper examines the predictors of consumer interest in and willing to pay for both AFVs and autonomous vehicles through a choice experiment conducted in six diverse markets: Germany, India, Japan, Sweden, UK and US. Using Latent Class Discrete Choice Models, we observe significant heterogeneity both within and across the country samples. For example, while Japanese consumers are generally willing to pay for autonomous vehicles, in most European countries, consumers need to be compensated for automation. Within countries, though, we found some segments – typically, those with a university degree, and self-identifying as having a pro-environmental identity and as being innovators– are more in favour of automation. Significantly, we also found that support for autonomous vehicles is associated with support for AFVs, perhaps, due to common demographic or socio-psychological predictors of both types of innovative technology. These findings are valuable for policymakers and the automotive industry in identifying potential early adopters, as well as consumer segments or cultures less convinced to adopt these innovative transport technologies.
The choice of battery range (all-electric driving range) for battery electric vehicles (BEVs) is an important issue for both BEV adopters and BEV makers. This paper proposes a model to identify the minimum BEV battery range that can satisfy given travel demands, considering the opportunities to charge at existing public charging stations and the uncertainties in charging decision making. We conducted a stated preference survey to study the charging decision making and analyzed the data using the Latent Class model to generate the model coefficients for charging decisions making. The proposed approach can better identify the needed battery range than the often-used simplified charging rules. We applied the model to a case study of Beijing to evaluate the needed battery range for taxis and private vehicles. For taxis, BEVs with 220-mile battery range are able to satisfy the travel demands for about 90% of the drivers. For private vehicles, a 300-mile range is needed to cover the travel demands of 90% of the drivers, while a 100-mile range battery is able to satisfy the need for 80% of the private drivers. Simplified charging rules tend to underestimate the range needs for taxis but overestimate the range needs for private vehicles.
Over the last decades the vehicle industry has shown interest in integrating new technologies on vehicles' design. Such technologies are used in autonomous, connected and electrical vehicles with the primary hope of improving road safety and the environmental impact of road traffic. Regarding the environmental impact, the transport sector has been considered responsible for Greenhouse Gas emissions for the past thirty years or more, and efforts have been made to reduce impacts of such emissions on the environment. The environmental noise is also associated with road traffic and its effects on public health, along with ways of scaling them down, have been under investigation. Taking into consideration worldwide efforts on climate change and new vehicle technologies that are being introduced, this paper provides a review on the studies concerning the environmental and traffic noise impacts anticipated by the implementation of these kinds of vehicles in the market and in road traffic. Two types of studies, conducted the last 10 years, are included in this review: a) studies that use logical estimates to draw conclusions on how Connected and Autonomous Vehicles (CAVs) as well as Electrical Vehicles will alter fuel consumption, gas emission, etc., and b) studies that make use of mathematical frameworks and the data available to extract numerical results. Eleven (11) factors affecting CAVs' environmental impacts were found and categorized based on whether they are related to the vehicle, the road network or the user. A comparison of the different procedures is attempted, in order to identify the factors that are influencing the emergence of anticipated environmental impacts as well as their variety and extent.
This paper describes development and testing of a passive GPS tracking smartphone application and corresponding data analysis methodology designed to increase the quality of travel behavior information collected in long-term travel surveys. The new approach is intended to replace the pencil-and-paper travel diaries and prompted recall methods that require more user involvement due to requirements for manual data entry and/or high battery usage. Reducing the burden placed on users enables researchers to collect data over longer periods, thus improving the quality of travel behavior research. To reduce battery use the smartphone-based application collects GPS data less frequently than other methods. Therefore, new algorithms were developed to identify trips and activities, transport mode, and even the specific vehicle used by the traveler. An important finding was the significant advantage of using users’ past data to improve mode detection results. The system was tested successfully in Zürich and Basel (Switzerland).
Autonomous vehicles (AVs) offer new technologies that could revolutionize travel, such as greater link capacity and innovative intersection controls. One such traveler behavior is the potential for empty repositioning trips, in which a vehicle travels without any passengers. Repositioning trips could allow travelers to avoid parking costs or make their vehicle available to other household members. However, empty repositioning trips increase the demand for personal vehicle travel. A previous study using static traffic assignment on home-to-work trips showed that repositioning trips still resulted in a net increase in congestion even when link capacity improvements for AVs were modeled. This raises the question of whether empty repositioning trips should be permitted. However, a key characteristic of repositioning trips is that they depart after the traveler has been dropped off. This could reduce the concentration of demand at any point in time. By using dynamic traffic assignment with a more realistic model of link flow on the downtown Austin network, we showed that when repositioning trips encourage travelers to switch to AVs, the resulting improvement from those AVs could decrease congestion. Furthermore, even if all vehicles are AVs, the congestion resulting from empty repositioning is still less than current conditions. Therefore, allowing empty repositioning trips could be beneficial for the traffic network.
Research on willingness to pay (WTP) can provide practical insights for assessing the value of self-driving vehicle (SDV) technology in the vehicle market. Are people willing to pay extra for the technology? What demographic and psychological factors can influence people's WTP for this technology? These questions are not yet well investigated. We conducted surveys in two cities in China (total N = 1355) and examined WTP and its potential demographic determinants (famil-iarity, age, gender, education, and income) and psychological determinants (perceived benefit and risk of SDVs, anticipated perceived dread riding in SDVs, and trust in SDVs). About 26.3% of participants were unwilling to pay extra, 39.3% were willing to pay less than 2900. Younger and highly educated participants with higher-income were willing to pay more. Participants who had heard about SDVs before the survey reported higher WTP and higher trust and perceived higher benefits, lower risks, and lower dread. Trust and perceived benefit were positive predictors of WTP and perceived risk and perceived dread were negative predictors of WTP. Our results may offer practical implications for increasing the public's acceptance and WTP of SDVs.
Travel demand modeling has evolved from the traditional four-step models to tour-based models which eventually became the basis of the advanced Activity-Based Models (ABM). The added value of the ABM over others is its ability to test various policy scenarios by considering the complete activity-travel pattern of individuals living in the region. However, the majority of the ABM restricts residents’ activities within the study area which results in distorted travel patterns. The external travel is modeled separately via external models which are insensitive to policy tests that an ABM is capable of analyzing. Consequently, to minimize external travel, transport planners tend to define a larger study area. This approach, however, requires huge resources which significantly deterred the worldwide penetration of ABM. To overcome these limitations, this study presents a framework to model residents’ travel and activities outside the study area as part of the complete activity-travel schedule. This is realized by including the Catchment Area (CA), a region outside the study area, in the destination choice models. Within the destination choice models, a top-level model is introduced that specifies for each activity its destination inside or outside the study area. For activities to be performed inside the study area, the detailed land use information is utilized to determine the exact location. However, for activities in the CA, another series of models are presented that use land use information obtained from open-source platforms in order to minimize the data collection efforts. These modifications are implemented in FEATHERS, an ABM operational for Flanders, Belgium and the methodology is tested on three medium-sized regions within Flanders. The results indicate improvements in the model outputs by defining medium-sized regions as study areas as compared to defining a large study area. Furthermore, the Points of Interests (POI) density is also found to be significant in many cases. Lastly, a comprehensive validation framework is presented to compare the results of the ABM for the medium-sized regions against the ABM for Flanders. The validation includes the (dis)aggregate distribution of activities, trips, and tours in time, space and structure (e.g. transport modes used and types of activities performed) through eleven measures. The results demonstrate similar distributions between the two ABM (i.e. ABM for medium-sized regions and for Flanders) and thus confirms the validity of the proposed methodology. This study, therefore, shall lead to the development of ABM for medium-sized regions.
This study analyzes the potential benefits and drawbacks of taxi sharing using agent-based modeling. New York City (NYC) taxis are examined as a case study to evaluate the advantages and disadvantages of ride sharing using both traditional taxis (with shifts) and shared autonomous taxis. Compared to existing studies analyzing ride sharing using NYC taxi data, our contributions are that (1) we proposed a model that incorporates individual heterogeneous preferences; (2) we compared traditional taxis to autonomous taxis; and (3) we examined the spatial change of service coverage due to ride sharing. Our results show that switching from traditional taxis to shared autonomous taxis can potentially reduce the fleet size by 59% while maintaining the service level and without significant increase in wait time for the riders. The benefit of ride sharing is significant with increased occupancy rate (from 1.2 to 3), decreased total travel distance (up to 55%), and reduced carbon emissions (up to 866 metric tonnes per day). Dynamic ride sharing, wich allows shared trips to be formed among many groups of riders, up to the taxi capacity, increases system flexibility. Constraining the sharing to be only between two groups limits the sharing participation to be at the 50–75% level. However, the reduced fleet from ride sharing and autonomous driving may cause taxis to focus on areas of higher demands and lower the service levels in the suburban regions of the city.
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.
The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity–travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity–Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.
Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.
Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50–90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be 0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and 0.05–0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.
The rapid growth of internet based ride-sharing brings great changes to residents' travel and city traffic. However, few studies had employed empirical data to examine the unique travel patterns of internet based ride-sharing trips. In this paper, we compare taxi trip records and internet based ride-sharing trip records provided by DiDi company. Results reveal many interesting findings that had never been reported before. From the viewpoint of service patterns, ride-sharing mainly increases supplies in hot areas and peak hours. By applying a non-negative matrix factorization method, we find that ride-sharing principally serves as an approach for commuting. So, as an effective supplement to traditional taxi service, it regulates spatial and temporal supply-demand imbalance, especially during morning and evening rush periods. From the viewpoint of individual behavior patterns, we use a clustering method to identify two kinds of internet based ride-sharing drivers. The first kind of drivers usually provides ride-sharing along daily home-work commuting. Trips served by these drivers have relatively constant origin-designation (OD) pairs. The second kind of drivers does not serve regularly and roams around the city even in working hours. Therefore, there are no constant OD pairs in their ride-sharing trips. Counterintuitively, we find that home-work commuting drivers account for only a small part of total drivers and they only serve a small number of commuting trips. In addition, internet based ride-sharing is not just traditional hitchhiking worked through mobile internet. We find that internet based ride-sharing drivers intend to make long distance trips, and they intend to detour further to pick up or drop off passengers than traditional hitchhike drivers since they are paid. All these findings are helpful for policy makers at all levels to make informed decisions about deployment of internet based ride-sharing service. This paper also verifies that big data analytics is particularly useful and powerful in the analysis of ride-sharing and taxi service patterns.
Keywords: Taxis, ride-sharing, travel pattern
The world is on the cusp of a new era in mobility given that the enabling technologies for autonomous vehicles (AVs) are almost ready for deployment and testing. Although the technological frontiers for deploying AVs are being crossed, transportation planners and engineers know far less about the potential impact of such technologies on urban form and land use patterns. This paper attempts to address those issues by simulating the operation of shared AVs (SAVs) in the city of Atlanta, Georgia, by using the real transportation network with calibrated link-level travel speeds and a travel demand origin–destination matrix. The model results suggest that the SAV system can reduce parking land by 4.5% in Atlanta at a 5% market penetration level. In charged-parking scenarios, parking demand will move from downtown to adjacent low-income neighborhoods. The results also reveal that policy makers may consider combining charged-parking policies with additional regulations to curb excessive vehicle miles traveled and alleviate potential social equity problems.