Traffic Stream Research Articles

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

Side-friction impacts on urban streets performance in divided and undivided streets

AbstractSide friction refers to combined variables indicating the degree of interaction between the activities and the traffic stream. The condition worsens when the transport demand and road-side activities increase, leading to inefficient traffic performance. This study has been focused on evaluating side friction impacts in terms of capacity and speed. Four links divided and undivided streets in Al-Najaf City, Iraq, were selected and on-street parking, pedestrian activities, entry-exit maneuvers, and temporary parking vehicles are considered as side friction elements. The results show about a 47% reduction in speed and about a 49% reduction in the capacity at a very high side friction level. Finally, a speed-predicting model has been developed for predicting the speed under side friction impacts.

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

Concept of heterogeneity index for urban mixed traffic

• This paper conceptualizes a new measure to quantify the heterogeneity of mixed traffic. A proportion-based variant of COV of dynamic PCU values is proposed for this purpose. • This study develops a ‘Gaussian Process Regression’-based classified speed model which is further used to estimate the PCU of different vehicle categories and determine the ‘Heterogeneity Index’ of the mixed traffic stream. • The study proposes the heterogeneity levels using the K-medoids clustering technique along with Davies-Bouldin Index and Silhouette Index as validation measures. • Sensitivity analysis performed in the study shows the effect of traffic volume, traffic composition, and classified speed on the proposed ‘Heterogeneity Index’. Heterogeneity is one of those characteristics which differentiate traffic conditions of a developing country from other developed nations. The heterogeneity which represents the diversity among vehicle categories is suspected to have adverse influences on lane discipline, congestion potential, and road users’ safety. However, the influence of heterogeneity on the above-mentioned parameters has been only measured indirectly by considering traffic composition as an indicator of the prevailed heterogeneity-level. No direct relationship between the heterogeneity and other parameters has been established as there is no methodology available yet for quantifying the heterogeneity of mixed traffic. The present study addresses this problem and conceptualizes the ‘Heterogeneity Index’ (HI) to quantify the heterogeneity present in a mixed traffic stream. HI is conceived as a measure of the dispersion of Passenger Car Units (PCU) for different vehicle categories from its central value. A higher value of HI signifies more diverse vehicle categories present in the traffic stream. PCU of a vehicle category was estimated using the speed-based method and the individual speeds were predicted based on classified volumes using the Gaussian Process Regression model developed in this study. This paper also recommends several categorical levels for easy perception about the intensity of heterogeneity. Further, the sensitivity analysis explored the dynamic aspects of HI. Results showcased how the HI of a traffic stream may vary subject to the combined or the individual change in traffic volume, traffic composition and classified speeds. The outcomes of the study will be useful to estimate the intensity of heterogeneity that prevailed within a mixed traffic stream with varying traffic conditions.

Simulation Studies of Link Group in Elastic Optical Networks Used in Internet of Things Solutions

Abstract The elastic optical network (EON) solution is the most flexible in terms of allocation of resources for various traffic streams transported in core part (edge-cloud) of Internet of Things (IoT) networks. This paper presents the results of a study on the determination of the occupancy distribution and the loss probability for particular traffic flows offered in EONs supporting IoT systems. The purpose of the research was to determine the impact of: the number of links making up a group, the numbers of frequency slot units (FSUs) in individual EON link, the traffic intensity of individual traffic flows, the capacity of the EON link (number of FSUs) and the number of FSUs requested by different traffic streams. The developed simulation program of link group of EON allows the calculation of the values of the occupancy distribution and loss probability. The results obtained from the simulation are presented on the graphs in the form of points with confidence intervals.

DDoS Attack Detection Using Enhanced Neural Network Algorithm in Software-Defined Networks

With the uncommon advancement of technology and networking, the world today is totally relying on it. The most obvious drawback of this technical progress resulted in several possible dangers. A Distributed Denial of Service (DDoS) attack is one in which a large number of compromised systems work together to prevent service from being provided to the targeted system. Consequently, to protect network servers, attack detection systems must be very effective. The proposed architecture has Software Defined Networks (SDN) which comprise controllers and SFlow agents. According to the article, the anomaly detection of statistical traffic, which is performed on both normal and pathological anomaly traces in the packet header, as well as traffic volume detection, is based on the suggested work provided in this study. The k-means clustering technique is used by the statistical anomaly detection system and the attack alert aggregation system. Low-level attack detection systems are used to generate cluster dissimilar warnings, which are then investigated further. Clusters generate meta-alerts based on the information they have gathered. After that, the report of meta-alerts is sent to the security specialists. The online alert aggregation technique, which is also known as the probabilistic model, is used to identify new assaults on a system. The k-means clustering method is used to improve the quality of the traffic data streams. The Enhanced Neural Network Algorithm (ENNA) is being used to advance the intelligent attack detection system, which is currently under development. It is utilized in open-day controllers to identify attacks with a 99.7% accuracy by using Mininet and the Python simulation tool (ODL). In future work, it is possible to evaluate how the suggested detection method would be used in the event of subnet attacks and its mitigation.

Basic characteristics of floating car data from the perspective of traffic loss during the COVID-19 pandemic

Data from floating vehicles is a modern technology and can be another source of data. There is a free data source available in the Czech Republic, which is relatively new. The addressed source of data from floating vehicles covers the whole Czech Republic, which is a promising source for future use e.g. in transport planning in logistics, estimation of travel times and other related issues. For this reason, it is appropriate to examine the qualitative parameters of the data to see if they characterize the traffic stream. The present paper deals with the size of the processed data. Furthermore, the paper compares the data quality and coverage. January data for four subsequent years was used. The period of the COVID19 pandemic, when traffic declined, was included. Finally, data from selected highways are compared and the period covered is evaluated.

Online Adaptive Generation of Critical Boundary Scenarios for Evaluation of Autonomous Vehicles

Testing and evaluation are critical steps in the development and deployment of autonomous vehicles (AVs). This paper aims to provide an online adaptive generation framework for critical boundary driving scenarios (CBDS) with flexible complexity and diversity. Both static scenarios and dynamic scenarios are comprehensively investigated based on real traffic flow data, where the generated static scenarios contain variables of drivable area, weather visibility, and road friction coefficient on autonomous vehicles, and the generated dynamic scenarios consider the effects of mixed traffic streams of AVs, human-driven vehicles, bicycles, and pedestrians. Two complexity models are proposed to characterize the complexity of static scenarios and dynamic scenarios separately, which help to construct feedback for the adaptive generation of CBDS. The scenario-based test is carried out on a joint simulation platform, and the multi-dimensional evaluation system, including safety, driving comfort, driving performance, and traffic coordination, is developed to assess the performance of AVs. Based on the proposed complexity models and multi-dimensional evaluation system, the generation of CBDS is transformed into online environmental parameter optimization. Moreover, the naturalistic driving scenarios generation, scenario complexity calculation, intelligent driving algorithm execution, and intelligent driving evaluation are all concatenated together to form a closed loop so as to adaptively generate critical boundary driving scenarios. Extensive simulations are conducted at the intersection with two different types of intelligent driving algorithms, which show the effectiveness of the proposed framework.

Traffic Flow State Analysis Considering Driver Response Time and V2V Communication Delay in Heterogeneous Traffic Environment

In order to study the heterogeneous traffic environment generated by connected automated vehicles (CAVs) and human-driven vehicles (HDVs), the car-following model and basic graph model of the mixed traffic flows of connected automated vehicles and human-driven vehicles are constructed. Considering driver response time and the communication delay of the connected automated vehicles control system, the three-parameter variation law of traffic flow is summarized to solve the traffic congestion problem in heterogeneous traffic environments. Firstly, the probability of six scenarios in queues of cooperative adaptive cruise control (CACC) vehicles, adaptive cruise control (ACC) vehicles, and human-driven vehicles in heterogeneous traffic environments is analyzed. The car-following model is defined, and the parameters are calibrated, and then a fundamental diagram model of traffic flow balance is derived. On this basis, considering driver response time and the communication delay of the linear controller, a car-following model considering multi-party delay is updated and established, and the heterogeneous traffic flow analysis of the two types of delays in the model is carried out. Finally, the microscopic simulation environment is constructed based on SUMO 1.17.0 (Simulation of Urban Mobility) software. The results show that when the permeability (the proportion of connected automated vehicles in a traffic stream) exceeds 0.6, CAVs account for the main part in the heterogeneous traffic, which has a positive impact on the maximum flow and the optimal density and can effectively improve the maximum capacity of the road. The simulation results show that the updated car-following model is reasonable and accurate in dealing with driver response time and V2V communication delay.

A theoretical model for evaluating the impact of connected and autonomous vehicles on the operational performance of turbo roundabouts

This article presents a methodology to estimate the entry capacity (EC) and total capacity (TC) of basic turbo roundabouts under partial and fully connected and autonomous vehicle (CAV) environments. EC calculations are partially based on capacity models and adjustment factors proposed by the HCM 7th edition, taking into account different proportions of CAVs in traffic streams. The proposed methodology was applied to a case study concerning a basic turbo roundabout with different traffic demands and market penetration levels (MPLs) of CAVs. It was assumed that the traffic stream consisted of 100% passenger cars with MPLs of CAVs ranging from 0% to 100%. The research proves that with the increase in MPLs of CAVs, ECs increase accordingly and delays and queues decrease. To maximize the TC, a control area was also hypothesized, where CAVs start to communicate with a turbo roundabout manager system. The system should be able to distribute and channel CAVs, and therefore the entering flows between entry lanes find the values of the maneuver distribution factors (α, β, γ, δ) between the right lane and the left lane of entries to maximize the TC for each origin–destination matrix of traffic flows.

Ultimately Bounded TSK Fuzzy Management in Urban Traffic Stream Mechanism: Multi-Agent Modeling Approach

In this paper, a novel control approach based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation, which the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large numbers of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a TS fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Floating car data (FCD) are a source of traffic information reported by GPS-enabled probes from within the traffic stream. FCD define speed, travel time and movement patterns, all key inputs for transport planning. FCD are readily available over the entire road network without roadside sensors and communications infrastructure, making this a particularly relevant traffic data source in low- and middle-income countries (LMICs) where resource limitations restrict the expansive distribution of traffic sensors. This review article aims to investigate the applicable use-cases of FCD in LMICs, while simultaneously sensitising transport practitioners to the potential data biases that may be present in FCD. Particularly, this paper considers whether the context of Sub-Saharan African countries allows for the application of FCD similarly to high-income country FCD use-cases, critically examining insights from literature. Sub-Saharan African countries typically differ from high-income nations with respect to urban form, population distribution (and segregation), modal choice, driver population, and technology penetration. To date, there has been no literature that concatenates the current practice of FCD use-cases in Sub-Saharan Africa, nor defines the fundamental differences between transport and land use planning in high- and low-income countries with the view to determine if these dissimilarities impact FCD collection and use. Literature revealed novel research involving the application of FCD to investigate informal public transport systems that are specific to Sub-Saharan Africa, while also determining that accuracy and potential biases of FCD are not widely understood in Sub-Saharan Africa. Findings show that biases are likely to occur in FCD in Sub-Saharan Africa because of the representation and behaviours of the sample reporting FCD, as well as the urban form of Sub-Saharan African cities. The paper concludes by developing a research agenda that will assist to further the knowledge of FCD sample bias and the applicable use-cases of FCD in Sub-Saharan Africa.

Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model

Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.

Age-dependent failure probabilities of corroding concrete sewer pipes under traffic loads

Concrete pipes are frequently used in the wastewater infrastructure. These buried structures are prone to ageing and deterioration, as well as damage under excessive stresses from traffic. This study presents the first set of temporal fragility models for underground concrete sewer pipes under the compounding effects of corrosion and truck loads. To address the high computational costs of fragility modeling, Bayesian Additive Regression Trees (BART) – a machine-learning based meta-model – are trained using a high-fidelity computational model of buried concrete pipes. The BART models are subsequently applied to develop age-dependent fragility models for serviceability and collapse of pipes conditional on truck loads. Considered uncertainties include statistically significant concrete and soil properties, truck load, and environmental factors that affect the corrosion of concrete sewer pipes. Results indicate that the direction of traffic stream, the degree of loss of wall thickness due to corrosion, and the maximum allowed truck load are critical factors that must be considered when planning inspection, rehabilitation, and retrofit of this underground infrastructure. According to the results, concrete sewer pipes buried in perpendicular to the direction of traffic flow are more vulnerable than those that are parallel to the direction of traffic flow. The results highlight that the probability of collapse is insignificant when it comes to vehicles weighing less than the design truck weight; however, the probability of collapse significantly increases for the moderate and severe corroded sewer pipes when it comes to the overweight vehicles. The age-dependent fragility models indicate that for sewer pipes buried perpendicular to the direction of traffic stream with service life of less than 25 years, the probability of the first major structural defect is less than 5%. This is also true for sewer pipes that have been in service for less than 45 years if the traffic stream is parallel to the buried pipe. The developed fragility models also constitute a key component of quantitative risk assessment of underground wastewater collection systems.

Evaluation of macroscopic fundamental diagram characteristics for a quantified penetration rate of autonomous vehicles

BackgroundThe availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models.PurposeSuch features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams.MethodologyThis paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis.ResultsCompared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%.ConclusionsResults obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates.

Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications

The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed “time spent in conflict (TSC)” is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs. The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs. The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.

Modelling braking behaviour of distracted young drivers in car-following interactions: A grouped random parameters duration model with heterogeneity-in-means

Braking is an important characteristic of driving behaviour that has a direct relationship with rear-end collisions in a car-following task. Braking becomes more crucial when drivers’ cognitive workload increases because of using mobile phones whilst driving. This study, therefore, investigates and compares the effects of using mobile phones whilst driving on braking behaviour. Thirty-two young licenced drivers, evenly split by gender, faced a safety–critical event, that is, leader’s hard braking, in a car-following situation. Each participant drove the CARRS-Q Advanced Driving Simulator and was required to respond to a braking event in the simulated environment in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A random parameters duration modelling approach is employed to (i) model drivers’ braking (or deceleration) times using a parametric survival model, (ii) capture unobserved heterogeneity associated with braking times, and (iii) account for repeated experiment design. The model identifies the handheld phone condition as a random parameter whilst vehicle dynamics variables, hands-free phone condition, and driver-specific variables are found as fixed parameters. The model suggests that most distracted drivers (in the handheld condition) reduce their initial speeds more slowly than undistracted drivers, reflecting their delayed initial braking that may lead to abrupt braking to avoid a rear-end collision. Further, another group of distracted drivers exhibits faster braking (in the handheld condition), recognising the risk associated with mobile phone usage and delayed initial braking. Provisional licence holders are found to be slower in reducing their initial speeds than open licence holders, indicating their risk-taking behaviour because of their less experience and more sensitivity to mobile phone distraction. Overall, mobile phone distraction appears to impair the braking behaviour of young drivers, which poses significant safety concerns for traffic streams.

Development of a Turning Movement Estimator Using CV Data

Turning movement (TM) data of vehicular traffic at intersections are a basic input for signal timing design. Existing methods of collecting TM data are time- and cost-intensive. Using connected vehicle (CV) data is an alternative method. Trajectories of vehicles through an intersection can be constructed using CV data. However, because of the low number of CVs in the traffic stream, it is imprecise to consider TM data from CVs as representative of the whole traffic flow. To address this issue, a Kalman filter (KF) for estimating TM rates at intersections based on CV data under low market penetration levels using commercially available connected vehicle data was developed in this study. This method is independent of intersection geometry or the presence of shared lanes. The algorithm was evaluated using data from an intersection in Salt Lake City, Utah. The manually collected TM counts at this intersection were compared with the raw CV data as well as the results obtained from the developed methodology. The comparison shows that while TM counts based on raw CV data show severe violations in accuracy, making them unreliable, the method developed in this research gives results that have much lower accuracy violations.

Differential impacts of autonomous and connected-autonomous vehicles on household residential location

High market penetration of autonomous vehicles (AVs) and connected-autonomous vehicles (CAVs) is expected to impact transportation network performance, which is an important determinant of residential location decisions, especially for households who commute to work by personal vehicle. This study examines and compares the impacts of privately owned AVs and CAVs on the location and commute characteristics as well as the spatial distribution of households within the Triangle Region in North Carolina. A Mixed Multinomial Logit model is developed using recent household survey data to capture household preferences. In addition, the region’s travel demand model, the Triangle Regional Model, is used to predict the network-level impacts of AV and CAV adoption, and cluster analysis is conducted to explore how network performance changes vary with transportation demand and supply zone characteristics at a local and regional level. Residential location patterns are predicted for a number of AV and CAV scenarios for the year 2045 using the outputs of the econometric analysis and the Triangle Regional Model. We find that extensive adoption of private CAVs improves network conditions and encourages households to live farther from work, leading up to a 5.6% increase in suburban and rural households that commute to work by personal vehicles. A high market share of AVs is associated with deteriorated transportation network performance and up to a 2.8% increase in urban households. Results vary by market penetration rate of each technology, mix of AVs, CAVs, and human driven vehicles in the traffic stream, and fuel type (conventional-fuel versus electric vehicles).