International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities

This study specifies and empirically tests an econometric model to examine the relationship between usage of information and communication technologies (ICT) by firms and measures of accessibility to transportation supplies, built environment, and business activity. The results show that better accessibility to local and regional transportation facilities is associated with a higher propensity of ICT usage by firms. Additionally, the likelihood of high business-related ICT usage is positively correlated with built environment attributes including employment density. Moreover, firms in sectors such as communication, information, real estate, and finance have a higher propensity of using ICT. These results provide a better understanding of the determinants of ICT usage by firms which can influence business-related travel and thereby, impact traffic congestion levels within an area. The findings can be used to develop firm-level policies regarding ICT usage based on new investments in transportation supplies or changes in the surrounding built environment.

Measuring trajectory similarity is a fundamental problem in the trajectory data mining field, and many similarity measurement methods had been proposed, such as dynamic time wrapping (DTW). However, these methods are dynamic programming problems, and dynamic programming problem usually leads to quadratic computational complexity. Thus, many acceleration algorithms were proposed. In this article, we proposed a deep neural network (DNN) based supervised similarity model, deep trajectory similarity model, to fit DTW similarity and to keep accuracy and orderliness. In the training process, we used low-frequency GPS trajectory data in Beijing as input data and used the DTW similarity of trajectory pairs as labels. In the test process, the model predicted the DTW similarity between two GPS trajectories. Experiments in this article indicated that deep trajectory similarity model could greatly decrease over 20% computation time than the acceleration algorithm of DTW similarity, FastDTW algorithm, and keep over 90% accuracy and over 97% orderliness. Experiments result indicated that the DTSM model has great potential in big data scenario.

Probe vehicle data is increasingly becoming widely available thanks to advancements in connected and automated vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. As a result, monitoring and estimating different network conditions such as speed and travel times based on this data have been proven to be greatly beneficial. However, there have been comparatively limited studies on extracting other critical traffic flow variables such as density and flow. These limited number of studies mostly develop algorithms to extract and exploit the somewhat regular patterns solely in fully stopped regions of the congested traffic. This paper proposes a more generalized microscopic approach that is applicable to a wider range of traffic conditions. A naïve Bayes model is implemented to estimate the number of unobserved vehicles in between a set of probe vehicles. The parameters needed for the naïve Bayes include means and standard deviations for the probability density functions (pdfs) for the distance headways between vehicles. The proposed model is tested based on the trajectory data collected from US 101 and I-80 in California for the FHWA’s NGSIM (next generation simulation) project. Under the various traffic conditions analyzed, the results show that the number of unobserved vehicles between two probes can be predicted with reasonably high accuracy for mixed traffic conditions.

Flooding that results from severe storms and hurricanes has a major impact on infrastructure, roadway network operations, and safety in the United States (U.S.). Increases in impervious area and climate change contribute to the rising impact of hydrologic events. These occurrences can be extremely disruptive and costly, often accelerating the deterioration of the infrastructure they impact. These impacts can be mitigated if the proper tools are in place to assess flood extent and the damage caused by flooding. Unmanned aerial systems (UAS) equipped with light detection and ranging (LiDAR) equipment may present an opportunity to effectively perform these large spatial assessments in a more timely and precise manner than current practices (e.g. visual inspection). However, the limitations of LiDAR technology in this application are not well-known and require further research. This study aims to investigate LiDAR data precision level by varying the targeted standing water depth and LiDAR location height. Results of this study are expected to lay the groundwork for further analysis of the potential for the use of UAS-based LiDAR systems in scanning and mapping standing water on roadways.

In this paper, we consider the positioning technology to facilitate the system services for automatic guided vehicle. Typically, quick response (QR) code is a feasible solution with high accuracy. However, the position information of QR code is discrete and sparse, which leads to uncertainty in position interpolation. To solve this problem, we proposed a positioning system of feature-based stereo visual odometry (VO) by combining some discrete and sparse absolute positioning information. In the proposed system, we adopt stereo cameras to obtain the depth of feature points directly and then apply an algorithm with two parallel threads of tracking and mapping. The mapping thread deals with not only information sent by tracking thread but also those sent by QR code. This proposed system has been tested and verified on our automatic guided vehicle. The measured and computational results suggest that the proposed system has acceptable robustness and relatively high accuracy.

This paper presents a study to reveal the mechanism of congestion impact on length-based vehicle classification using dual-loop data. Both ground-truth vehicle trajectory and simultaneous loop event data are used to characterize the impact of congested traffic on vehicle classification. Eight scenarios are synthesized to define the vehicles’ stopping locations over two single loops. Under the synchronized traffic, acceleration or deceleration is considered in the new developed vehicle classification under synchronized traffic model. The error of vehicle classification is reduced from 33.5% to 6.7%, compared to the existing applied model. Under the stop-and-go traffic condition, a stop-on-both-loops-only (SBL) was developed to simplify the complexity of congested traffic situation, resulting in the error from 235% to 17.1%. To identify traffic congestion states using the dual-loop data, an innovative method for identifying the traffic phases has been proposed based relationship of speed and occupancy, and then successfully tested with the experimental data.

To facilitate a sustainable transformation that aligns with economic, political, and ideological elements of our societies, this is an auspicious idea to have a deliberated planning for our automated fleets. A crucial item in such vehicles is providing a significant efficiency for users such that it competes with the status quo efficiency of internal combustion engine vehicles (ICEVs) with petroleum fuels. The charging time for fully hydrogen fuel cell vehicles (HFCVs) is a hindrance for the market penetration of those cars. Moreover, planning for refueling and charging stations should be prioritized in this matter due to the fact that those topics are intertwined with one another. The main hindrance for market penetration of hydrogen fuel cell vehicles at the right moment is range anxiety. In other words, the drivers are not sure if they can make it to the next refueling/recharging station with the ongoing number of charging lots. This study investigates the opportunities and challenges associated with different automated vehicles’ refueling/recharging systems and the provided efficiency of each system. Furthermore, the authors provide some insights on allocating the refueling and charging stations according to each system.

An emerging technology in transportation, electric vehicles (EVs) decrease reliance on pollutant fuels and bring numerous environmental benefits. The current research investigated the socio-demographic attributes that may contribute to EV ownership and owners’ purchasing behavior. The goal of this study is providing public policies and recommendations to decision makers to prompt equitable EV usage by identifying influencing socio-demographic attributes of EV purchasing behavior. This study surveyed registered plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) owners in Maryland from May 28, 2015, to February 19, 2016. In total, 1,257 EV owners in Maryland completed usable surveys. A set of statistical analysis methods was employed to analyze the data. Researchers constructed a multinomial logistic model (MNL) to examine the associations between EV owner characteristics and their reasons for purchasing/leasing the EV, and an analysis of variance test (ANOVA) was carried out to explore possible relationships between EV owners’ travel behavior and their socio-demographic features. Findings suggest that, first, age, education, income, household size, number of vehicles in the household, marital status, and political affiliation play a significant role in preferences attributes of participants for purchasing/leasing an EV. Second, environmental issues are the main reason for purchasing/leasing EVs, but the EV owners who had longer commutes were more concerned about the price and status of the EV owner and efficiency and performance than were those with shorter commutes. Third, EV owners who are older and more educated, drive less than other groups.

Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates the fuel tax revenue impacts of such trends using Virginia as a case study. First, a county-level bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts while more households with children is associated with fewer EVs. The analysis forecasts 0.6% to 10% statewide EV adoption by 2025, with an adoption rate of 2.4% in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project statewide fuel tax revenue to decrease by 5% to 19%, relative to 2016 receipts. Furthermore, though all counties are predicted to experience decreasing fuel tax revenue contribution per vehicle (due to fuel economy improvements and EV adoption), the decrease is more significant in urban areas. Model results predict that on average a light duty vehicle in a rural area will pay 28% more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.

Recent technological advancements in mobility are creating many options for connecting citizens with employment, goods, and services, particularly in urban areas where modes such as bike and car shares, electric scooters, ridesourcing, and ridesharing are proliferating at a rapid pace. Analysis and tools for overall transportation planning are dominated by urban regional travel demand models whose roots in highway operations poorly reflect the system dynamics in denser areas where parking costs, convenience, and availability—not to mention sustainability concerns and quality of life—are driving people to an ever-greater spectrum of mobility services. In this paper, we present a new paradigm for evaluating mobility options within an urban area. First developed for the U.S. Department of Energy’s Energy Efficient Mobility System research program, this metric is termed the mobility-energy productivity (MEP) metric. At its heart, the MEP metric measures accessibility and appropriately weights it with travel time, cost, and energy of modes that provide access to opportunities in any given location. The proposed metric is versatile in that it can be computed from readily available data sources or derived from outputs of regional travel demand models. End times associated with parking, curb access, cost, and reliability and frequency of service need to be carefully considered to obtain an appropriate and accurate perspective when computing the metric. Ultimately, the MEP metric can be used to reflect the impacts of new mobility technologies (transportation network companies, electric scooters), business models (car shares and bike shares), and land-use practices (such as transit-oriented development) on sustainable urban mobility. This paper lays out the need, requirements, and framework for this new metric, and offers it, in collaboration with the American Society for Civil Engineers (ASCE), as a foundational metric for smart city assessment.

In this paper, two categories have been considered: optimizing economic issues in long-term planning of regional airline network structure; and the importance of changes in demand in different months of the year in airline network design. A mixed integer linear programming model has been provided in this study which, compared to previous models, considers issues such as capacity constraints and network planning issues. Moreover, the most important initiative of this article is entering two parameters: cost (non-profit) due to losing customers and expenditures of the unused capacity. These two parameters will help the model identify the best supply in each path. Various demand functions have been used in solving the model. Based on conducted analyses, using the expected value of monthly demand in comparison with high and low demand and seasonal changes in the network is optimized. The results of study also show that in times of high demand, the airline tends to aggregate passengers and increase density in hub paths.

Traffic simulation models can provide a cost-effective and flexible alternative to evaluate the various roadway designs and traffic management alternatives. To do so, we first need to ensure that the models are appropriately calibrated to reflect the existing conditions. The use of unmanned aerial vehicle (UAV) has gained particular attention in the transportation infrastructure industry. On a nationwide scale, there has been a trend to incorporate UAV in traffic operations such as traffic monitoring and infrastructure management activities which include bridge and roadway inspections. The goal of this study is to evaluate the effectiveness of utilizing UAV to validate traffic simulation model outputs. To that end, two simulation models of an intersection in Auburn, Alabama, were developed using Synchro and VISSIM simulation software. The high-resolution video and photographic data collected by UAV were employed to validate that the models reflect the existing traffic operations at the study intersection. Simulation models were calibrated and compared with results obtained from drone videos. The results showed that UAVs have the potential to improve the validation procedure of traffic simulation by providing complete pictures of traffic movements at entire intersections.

Unmanned system technology is an emerging field in the world of civil engineering. While there are many possible applications for using unmanned systems for data collection there are currently many restrictions. This paper provides insight to a cost effective utilization of unmanned systems to complete data collection for transportation projects and discusses in detail the laws currently limiting unmanned system use in the United States.

This study has developed an approach for evaluating existing transport network infrastructure in terms of connectivity, travel demand, and crime. An advantage of the methods described in this study is its relative simplicity and flexibility to generate resources to help inform decision-making and advocacy efforts with the goal of promoting equitable infrastructure investment that will help create healthy, dynamic, and livable communities for all. The analysis reveals that in some cases disparities exist with access to public transit infrastructure as Baltimore’s minority, low income, elderly, and youth populations have limited access to these facilities compared to others. As disparities may occur without intent, this approach may assist the city to evaluate their practices to ensure that inappropriate considerations do not affect their judgment. This study reveals that strategic investment in underserved neighborhoods hold promise for increasing access to jobs and opportunities, travel options, and an equitable distribution of infrastructure.

Urban governments show great interest in formulating policies for the sustainable transport sector. Many tend to integrate environmental, economic, and social impacts into transport decision-making, and develop a system capable of achieving all transport objectives which are: accessibility, safety, affordability, and environmentally friendly. The purpose of this study is to measure the sustainability of Amman transportation system based on United States environmental protection agency guidelines, using four performance measures: transit accessibility, vehicle miles traveled (VMT) per capita, carbon intensity, and transit productivity. Wadi Al-sair and Al Abdali Districts were selected to represent Amman. Accessibility was evaluated using GIS software; estimation of VMT per capita, carbon intensity, and transit productivity were mainly based on data obtained from field survey. The results reveal that accessibility is good in the study area, while VMT per capita was found to be lower than in the United States, carbon intensity was higher than the world average, while transit productivity was higher than in developed countries. The study concluded that the system needs improvement in all measured areas to go towards sustainability, in addition to a significant reliance on private transit at the expense of public transport. Public transport must be improved to encourage people to move towards it. As well as the adoption of intelligent land-use growth, such as comprehensive urban and regional planning, including compact cities, mixed land use, and transit oriented development.

The public transport sector demand in Indian cities has been growing continuously. The Government of India has proposed many transit systems like mass rapid transit system (MRTS), bus rapid transit system (BRTS), and light rail transit system (LRTS), etc. in every big city to address these demands. However, the connectivity of the first-last mile to these terminals is not adequately taken care of. Intermediate para-transit (IPT) like auto rickshaws (three wheeled motor vehicle), cycle-rickshaws (three wheeled cycle), and recently evolved e-rickshaws (battery operated three-wheeled vehicle) serve most of the cases for first-last mile connectivity. In these days, e-rickshaws are serving as the last mile connectivity (LMC) modes at various MRTS stations but have evolved as a response of need by the common people. Due to absence of rule and norms, it is largely being operated informally. Integration of e-rickshaws into the system at the policy as well as at the infrastructure level is needed for the development of an appropriate feeder system. The paper discusses about the need of directions for the seamless operation and integration of the E-rickshaws as the important mode of IPT and LMC option for Delhi in particular and other similar cities in general.

This paper compares urban transportation solutions that are currently in use or under consideration including personal rapid transit (PRT), bus rapid transit (BRT), light rail transit (LRT), electric bicycle rapid transit (BikeRT), transportation network companies (TNC) like Uber and Lyft, and small driverless shuttles (A-shuttles). Since these solutions are often presented without an evaluation of which problems they address and which they ignore, or even exacerbate, the paper develops a methodology to evaluate and compare them. A simplified systems-engineering analysis is undertaken whereby the fundamental requirements for an effective 21st century transportation system are derived from the results of a series of public workshops. These requirements include: safe and secure; reliable; cost-effective; reduces VMT and congestion; sustainable; ADA compliant; widespread service area. Goals are developed for each requirement. The ability of each of the solutions to meet the goal for each requirement is then considered and rated. The results are summarized and ways of combining solutions considered. It is suggested that those questioning the presented results use the same process to develop their own list of requirements and score their preferred solutions against those requirements.