arXiv subjects

Bilal Farooq

Publications and source records attributed to Bilal Farooq.

At least 37 records · Page 2Linked to original sources

arXiv2025

A Coalition Game for On-demand Multi-modal 3D Automated Delivery System

We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks to address last-mile delivery in urban environments, including high-density areas and time-critical applications. The problem is defined as multiple depot pickup and delivery with time windows constrained over operational restrictions, such as vehicle battery limitation, precedence time window, and building obstruction. Utilizing the coalition game theory, we investigate cooperation structures among the modes to capture how strategic collaboration can improve overall routing efficiency. To do so, a generalized reinforcement learning model is designed to evaluate the cost-sharing and allocation to different modes to learn the cooperative behaviour with respect to various realistic scenarios. Our methodology leverages an end-to-end deep multi-agent policy gradient method augmented by a novel spatio-temporal adjacency neighbourhood graph attention network using a heterogeneous edge-enhanced attention model and transformer architecture. Several numerical experiments on last-mile delivery applications have been conducted, showing the results from the case study in the city of Mississauga, which shows that despite the incorporation of an extensive network in the graph for two modes and a complex training structure, the model addresses realistic operational constraints and achieves high-quality solutions compared with the existing transformer-based and classical methods. It can perform well on non-homogeneous data distribution, generalizes well on different scales and configurations, and demonstrates a robust cooperative performance under stochastic scenarios across various tasks, which is effectively reflected by coalition analysis and cost allocation to signify the advantage of cooperation.

cs.LG↗

arXiv2025

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs

We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure represented by a set of nodes (representing buildings), connected through a route of edges or links using a novel Graph Convolution plus LSTM Neural Network (GCLSTM) which has shown remarkable success in modelling complex patterns. We describe the formulation, model estimation, interpretability and examine the relative performance of our proposed model. We also present an illustrative architecture of the models and apply on real-world WiFi logs collected at the Toronto Metropolitan University campus. The results of the experiments show that the integrated GCLSTM models significantly outperform traditional pedestrian flow estimators like the Multi Layer Perceptron (MLP) and Linear Regression.

cs.LG↗

arXiv2025

Sphereabout: A Spherical Intersection Design for Networked Urban Air Mobility

Urban aerial mobility is rapidly expanding, specifically on-demand Unmanned Aerial Vehicle (UAV) delivery services in urban environments. This necessitates management of the low-altitude airspace network to ensure smooth and safe traffic throughput. This study introduces a novel three-dimensional aerial network design inspired by a terrestrial transportation graph network for UAV networked mobility. Utilizing two-way tube corridors for node-to-node delivery and a spherical roundabout model, as "Sphereabout", to optimize the traffic flow through the spherical intersection. Through this architecture, three-dimensional conflict-free air mobility management can be achieved via numerical experiments and utilizing the geometrical features of this intersection.

math.OC↗

arXiv2025

Towards the Safety-Relevant Dimension of Driver Behaviour: A Dual-State Model

We make a methodological contribution by introducing a new dimension of traffic conflict severity: the probability that a driver is in a defensive state. This behavioural probability reflects an internal response to perceived risk and is estimated using a latent class Discrete Choice Model (DCM) that captures driver behaviour as a probabilistic mixture of two latent driving states: a defensive state, representing heightened caution and collision-avoidance intentions under perceived risk, and a neutral state, reflecting routine driving behaviour under low-threat conditions. The framework is grounded in psychological theory, particularly the triad of affect, behaviour, and cognition. It is also informed by two key concepts. First, that event severity exists on a continuum, rather than being confined to binary categories of safe or unsafe. Second, that drivers perceive risk through a dynamic spatial safety field, one that varies with direction, proximity, and the motion of surrounding road users. Applied to the publicly available rounD dataset, the framework yields interpretable estimates of state membership probabilities. The defensive state consistently reflects stronger sensitivity to spatial and temporal risk, while the neutral state captures context-appropriate yet less reactive driving patterns. Importantly, the paper also proposes a method to assess the quality of the estimated probability of being in a defensive state. Because of the duality between the defensive and neutral states, evaluating the consistency of one offers insights into the reliability of the other. To explore this, a multi-step validation procedure is applied across five data subsets representing different driving contexts, including free-flow and diverging scenarios, to examine how well the neutral state generalises beyond the estimation sample.

physics.soc-ph↗

arXiv2025

Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto

As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study explores decarbonization strategies for urban traffic in downtown Toronto through microsimulation, evaluating the environmental and economic impacts of vehicle technologies, traffic management strategies (eco-routing), and driving behaviours (eco-driving). The study analyzes 140 decarbonization scenarios involving different fuel types, Connected and Automated Vehicle (CAV) penetration rates, and anticipatory routing strategies. Using transformer-based prediction models, we forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, along with average speed and travel time. The key findings show that 100% Battery Electric Vehicles (BEVs) reduce GHG emissions by 75%, but face challenges related to cost and infrastructure. Hybrid Electric Vehicles (HEVs) achieve GHG reductions of 35-40%, while e-fuels result in modest reductions of 5%. Integrating CAVs with anticipatory routing strategies significantly reduces GHG emissions. Additionally, eco-driving practices and eco-routing strategies have a notable impact on NOx emissions and travel time. By incorporating a comprehensive cost analysis, the study offers valuable insights into the economic feasibility of these strategies. The findings provide practical guidance for policymakers and stakeholders in developing effective decarbonization policies and supporting sustainable transportation systems.

cs.CY↗

arXiv2024

Decoding Pedestrian Stress on Urban Streets using Electrodermal Activity Monitoring in Virtual Immersive Reality

The pedestrian stress level is shown to significantly influence human cognitive processes and, subsequently, decision-making, e.g., the decision to select a gap and cross a street. This paper systematically studies the stress experienced by a pedestrian when crossing a street under different experimental manipulations by monitoring the ElectroDermal Activity (EDA) using the Galvanic Skin Response (GSR) sensor. To fulfil the research objectives, a dynamic and immersive virtual reality (VR) platform was used, which is suitable for eliciting and capturing pedestrian's emotional responses in conjunction with monitoring their EDA. A total of 171 individuals participated in the experiment, tasked to cross a two-way street at mid-block with no signal control. Mixed effects models were employed to compare the influence of socio-demographics, social influence, vehicle technology, environment, road design, and traffic variables on the stress levels of the participants. The results indicated that having a street median in the middle of the road operates as a refuge and significantly reduced stress. Younger participants were (18-24 years) calmer than the relatively older participants (55-65 years). Arousal levels were higher when it came to the characteristics of the avatar (virtual pedestrian) in the simulation, especially for those avatars with adventurous traits. The pedestrian location influenced stress since the stress was higher on the street while crossing than waiting on the sidewalk. Significant causes of arousal were fear of accidents and an actual accident for pedestrians. The estimated random effects show a high degree of physical and mental learning by the participants while going through the scenarios.

cs.CY↗

arXiv2024

Simulation Models for Sustainable, Resilient, and Optimized Global Electric Vehicles Supply Chain

While the transition to electric vehicles (EVs) is essential for decarbonizing the transportation system, the production and distribution of EVs entail substantial carbon costs. To ensure these emissions are accurately accounted for and effectively mitigated, this research introduces a digital twin of the EV's supply chain, addressing a critical gap in current EV life cycle analyses and providing the first comprehensive quantification of its environmental sustainability and resilience. This simulation model replicates global market dynamics and captures the complexity and uncertainty of the EV supply chain, enabling a thorough evaluation of its carbon footprint, sustainability, resilience, and what-if counterfactual scenarios for alternative market structures. The results reveal that average supply chain emissions range from 6.42 to 6.94 Kg e-CO2/KWh across different battery technologies. Additionally, the mass flow analysis shows unbalanced dependencies at all supply phases, with one geographical region significantly dominating the supply chain structure, highlighting the current supply chain architecture's low resilience and high vulnerability. In light of these findings, the study introduces an optimization model for hub and resource allocation configuration, effectively reducing vulnerability levels and supply chain emissions by up to 80%.

math.OC↗

arXiv2024

A deep causal inference model for fully-interpretable travel behaviour analysis

Transport policy assessment often involves causal questions, yet the causal inference capabilities of traditional travel behavioural models are at best limited. We present the deep CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a framework that explicitly models causality in travel behaviour, enhances predictive accuracy, and maintains interpretability by leveraging causal inference, deep learning, and traditional discrete choice modelling. Within this framework, we introduce a Generative Counterfactual model for forecasting human behaviour by adapting the Normalizing Flow method. Through the case studies of virtual reality-based pedestrian crossing behaviour, revealed preference travel behaviour from London, and synthetic data, we demonstrate the effectiveness of our proposed models in uncovering causal relationships, prediction accuracy, and assessing policy interventions. Our results show that intervention mechanisms that can reduce pedestrian stress levels lead to a 38.5% increase in individuals experiencing shorter waiting times. Reducing the travel distances in London results in a 47% increase in sustainable travel modes.

cs.LG↗

arXiv2024

Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems

The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were not present in traditional fixed networks. These threats arise due to the intrinsic nature of wireless transmission medium and other inherent characteristics of mobile networks such as high-node mobility and rapidly changing topology. This paper investigates the robustness of Vehicular AttestedFL defense strategies against falsified information attacks by tracking the behavior. We show that the defense strategies are capable of detecting and eliminating malicious nodes in the wireless mobile setting of the future smart road networks.

eess.SY↗

arXiv2024

Decoding Driver Takeover Behaviour in Conditional Automation with Immersive Virtual Reality

The safe transition from conditional automation to manual driving control is significantly intertwined with the vehicle's lateral and longitudinal dynamics. The transition may occur as a result of a system-initiated mandatory takeover (MTOR) or as a driver-initiated discretionary takeover (DTOR). In either condition, the takeover process entails differing cognitive demands and may affect the driving behaviour differently. This study analyzes driving stability and perceived mental workload in 304 takeover attempts recorded from 104 participants within virtual and immersive reality environments. Adopting an exploratory approach, this dynamic simulator study employs a mixed factorial design. Utilizing a deep neural network-based survival analysis with SHAP interpretability, the study investigated the influence of covariates on perception-reaction time (PRT), distinguishing between safe and unsafe control transition and offering insights into the temporal dynamics of these shifts. The distributions of key parameters in experimental groups were analyzed and factors influencing the perceived mental workload were estimated using multivariate linear regression. The findings indicate a notable decrease in the risk of unsafe takeovers (described by a longer PRT) when drivers have prior control-transition experience and familiarity with Automated Vehicles (AVs). However, driver's prior familiarity and experience with AVs only decreased the perceived mental workload associated with DTOR, with an insignificant impact on the cognitive demand of MTOR. Furthermore, multitasking during automated driving significantly elevated the cognitive demand linked to DTOR and led to longer PRT in MTOR situations.

cs.HC↗

arXiv2023

Driver Heterogeneity in Willingness to Give Control to Conditional Automation

The driver's willingness to give (WTG) control in conditionally automated driving is assessed in a virtual reality based driving-rig, through their choice to give away driving control and through the extent to which automated driving is adopted in a mixed-traffic environment. Within- and across-class unobserved heterogeneity and locus of control variations are taken into account. The choice of giving away control is modelled using the mixed logit (MIXL) and mixed latent class (LCML) model. The significant latent segments of the locus of control are developed into internalizers and externalizers by the latent class model (LCM) based on the taste heterogeneity identified from the MIXL model. Results suggest that drivers choose to "giveAway" control of the vehicle when greater concentration/attentiveness is required (e.g., in the nighttime) or when they are interested in performing a non-driving-related task (NDRT). In addition, it is observed that internalizers demonstrate more heterogeneity compared to externalizers in terms of WTG.

cs.HC↗

arXiv2023

On-Demand Transit User Preference Analysis using Hybrid Choice Models

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help the municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT service and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

econ.EM↗

arXiv2023

Sustainability Analysis Framework for On-Demand Public Transit Systems

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

cs.CY↗

arXiv2023

Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads

Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportation decision-makers. In this study, an Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic volume and speed in a critical rural road segmentation which connects Tehran to Chalus, the most tourist destination city in Iran. Moreover, this study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model. Both models show acceptable performance in predicting speed and flow. However, the A-LSTM model outperforms the LSTM in 5 and 15-minute intervals. In contrast, there is no meaningful difference between the two models for the 30-minute time interval. By comparing the performance of the models based on different time horizons, the 15-minute horizon model outperforms the others by reaching the lowest Mean Square Error (MSE) loss of 0.0032, followed by the 30 and 5-minutes horizons with 0.004 and 0.0051, respectively. In addition, this study compares the results of the models based on two transformations of temporal categorical input variables, one-hot or cyclic, for the 15-minute time interval. The results demonstrate that both LSTM and A-LSTM with cyclic feature encoding outperform those with one-hot feature encoding.

cs.LG↗

arXiv2022

Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour

Before the transition of AVs to urban roads and subsequently unprecedented changes in traffic conditions, evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a Double/Debiased Machine Learning (DML) model in which the impact of confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature, for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In DML model structure, the standard error term of density parameter is lower than copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, due to the spurious effect of confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.

cs.LG↗

arXiv2022

Robustness Analysis of Deep Learning Models for Population Synthesis

Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional sample. The implementation of population synthesis on single datasets is seen as a drawback that needs further studies to explore the robustness of the models on multiple datasets. While comparing with the real data can increase trust and interpretability of the models, techniques to evaluate deep generative models' robustness for population synthesis remain underexplored. In this study, we present bootstrap confidence interval for the deep generative models, an approach that computes efficient confidence intervals for mean errors predictions to evaluate the robustness of the models to multiple datasets. Specifically, we adopt the tabular-based Composite Travel Generative Adversarial Network (CTGAN) and Variational Autoencoder (VAE), to estimate the distribution of the population, by generating agents that have tabular data using several samples over time from the same study area. The models are implemented on multiple travel diaries of Montreal Origin- Destination Survey of 2008, 2013, and 2018 and compare the predictive performance under varying sample sizes from multiple surveys. Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets of the varying sample sizes when compared to VAE. Again, the evaluation of model robustness against varying sample size shows a minimal decrease in model performance with decrease in sample size. This study directly supports agent-based modelling by enabling finer synthetic generation of populations in a reliable environment.

cs.LG↗

arXiv2022

Distributed Ride-Matching for Shared Ridehailing Service with Intelligent City Infrastructure

High computational time is one of the most important operational issues in centralized dynamic shared ridehailing services. To resolve this issue, we propose a distributed ride-matching system that is based on vehicle to infrastructure (V2I) and infrastructure to infrastructure (I2I) communication. The application on downtown Toronto road network demonstrated that the distributed system resulted in a speed-up of 125 times in terms of computational time and showed high scalability. Moreover, the service rate in the proposed system improved by 7% compared to the centralized. However, the centralized system showed 29% and 17% improvement in wait time and detour time, respectively.

cs.DC↗

arXiv2022

Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level

To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, especially in populated urban areas, substantial effort has been going into building more accurate and informative GHG prediction models to help create more sustainable urban environments. In this work, we seek to establish a predictive framework of GHG emissions at the urban road segment or link level of transportation networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner. Our results show up to 85.4% prediction accuracy in the DCM models' performances. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework.

econ.EM↗