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Found 38 papers, 2 papers with code
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.
Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads
Moreover, this study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model.
Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour
However, we mainly investigate the causal effect of traffic density on pedestrian waiting time.
Robustness Analysis of Deep Learning Models for Population Synthesis
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.
Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level
We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner.
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode.
Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices
We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as a binary classification algorithm, to develop a fully interpretable deep learning-based ordinal regression model.
The Impact of COVID-19 Pandemic on Ridesourcing Services Differed Between Small Towns and Large Cities
The COVID-19 pandemic has significantly influenced all modes of transportation.
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.
On the Initial Behavior Monitoring Issues in Federated Learning
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief.
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems
We identified a number of attack strategies conducted by the malicious CAVs to disrupt the training of the global model in vehicular networks.
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.
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation
We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious worker.
A generalized ride-matching approach for sustainable shared mobility
It starts with a one-to-one matching in Step 1 and is followed by solving a maximum weight matching problem in Step 2 to combine the travel requests.
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches.
Smart Mobility Ontology: Current Trends and Future Directions
Ontology is the explicit and formal representation of the concepts in a domain and relations among them.
Spatio-Temporal Analysis of On Demand Transit: A Case Study of Belleville, Canada
We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019.
Computers and Society
Resilience-by-design in Adaptive Multi-Agent Traffic Control Systems
Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITS), such as Traffic Signal Control (TSC) for urban traffic congestion management.
Autonomous Vehicles
Cryptography and Security
Interpretable Data-Driven Demand Modelling for On-Demand Transit Services
On the other hand, the demographic characteristics of the trip destination were the most important variables in the trip distribution model.
Prediction of Traffic Flow via Connected Vehicles
To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory.
Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
Whether myopic or proactive, the multi-objective routing, with travel time and GHG minimization as objectives, outperformed the single objective routing strategies, causing a reduction in the average travel time (TT), average vehicle kilometre travelled (VKT), total GHG and total NOx by 17%, 21%, 18%, and 20%, respectively.
Greenhouse Gas Emission Prediction on Road Network using Deep Sequence Learning
Mitigating the substantial undesirable impact of transportation systems on the environment is paramount.
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entire populations, data that are not typically readily available.
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning
This study investigates pedestrian crossing behaviour, as an important element of urban dynamics that is expected to be affected by the presence of automated vehicles.
ResLogit: A residual neural network logit model for data-driven choice modelling
This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model.
Information processing constraints in travel behaviour modelling: A generative learning approach
We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations.
A multi-layered blockchain framework for smart mobility data-markets
A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges.
Computers and Society
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey
In our final model, we combine the output of CNN models using "average voting", "majority voting" and "optimal weights" methods.
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis
Pedestrian's road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles.
Multi-Objective Autonomous Braking System using Naturalistic Dataset
A deep reinforcement learning based multi-objective autonomous braking system is presented.
Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data.
A semi-supervised deep residual network for mode detection in Wi-Fi signals
Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation.
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data
We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective.
Mobility Mode Detection Using WiFi Signals
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i. e. walking, biking and driving.
A blockchain framework for smart mobility
A blockchain framework is presented for addressing the privacy and security challenges associated with the Big Data in smart mobility.
Computers and Society
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences -- safety, comfort, and environmental.
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process.
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services.
