The rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area.
The development of 6G enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.
Abstract Analyzing route choice behavior and understanding the heterogeneous impacts on passenger decisions are key to improving transportation service quality in integrated suburban railway and metro networks. While existing studies focus on metro systems using Mixed Logit (MXL) models or clustering methods with Multinomial Logit models, these approaches struggle to capture the diverse factors in travel scenarios and the complex nonlinear relationships in decision-making. This study integrates Latent Dirichlet Allocation with Machine-Learning models like eXtreme Gradient Boosting (XGBoost), Support Vector Machine, and Random Forest to analyze passenger behavior within Shanghai’s composite rail network. It identifies key influencing factors such as socio-economic demographic characteristics, travel purposes, and the express-to-local departure ratio. XGBoost outperforms other models, including the traditional MXL, in predictive performance. The study highlights significant heterogeneity in route choices across different passenger groups, underscoring the need for personalized transportation solutions. Based on these findings, this study proposes the following actionable suggestions for suburban railway in the composite network: In terms of operational optimization, it is proposed to add express train overtaking stations in key commuting corridors and optimize the timetable to reduce transfer waiting times, thereby improving overall travel efficiency. Besides, time-of-day differentiated pricing and combined-ticket discounts are proposed to improve the rationality of the ticket-price structure. Finally, it is recommended to enhance the personalized route recommendation system.
Transportation engineering, Transportation and communications
Abstract In urban rail flexible traction power supply system (FTPSS), conventional energy-saving strategies for reversible converter (RC) predominantly rely on offline optimization with fixed parameters. However, inherent uncertainties in train operations, such as timetable deviations and stochastic load fluctuations, result in energy consumption volatility, rendering traditional approaches suboptimal. To address this, we propose a multi-timescale model predictive control (MPC) framework that integrates day-ahead scheduling and intraday rolling optimization. Second, we propose a novel data processing method for neural network training in the intraday to construct a neural network-based load prediction model, which is used as the model prediction control (MPC) input for rolling optimization. Validated on Qingdao Metro Line 11 datasets, the prediction model achieves a correlation coefficient (R 2) value of 95.2%, and the mean squared error (MSE) is 0.078, outperforming conventional prediction methods. By integrating MPC-based rolling optimization with day-ahead scheduling, the proposed strategy improves the energy-saving rate by 2.00% over traditional offline optimization methods. Demonstrating robustness against timetable perturbations and load uncertainties.
Transportation engineering, Transportation and communications
Arika Ligmann-Zielinska, Igor Vojnovic, Timothy F. LeDoux
This paper presents a data-driven agent-based model that simulates the weekly grocery shopping behavior of disadvantaged consumers in highly segregated lower eastside neighborhoods of Detroit, Michigan. We focus on neighborhoods experiencing severe disinvestment to analyze the shopping behavior of residents after all major regional and national supermarket chains abandoned the city. The presented model is unique in that it utilizes detailed shopping behavior data collected to examine travel in marginalized communities, specifically among residents in severe poverty who are often overlooked in the travel behavior literature. The research shows that in extreme socio-economic decline, sociodemographic variables (such as class) can become more relevant than the built environment (land-use mix, density, and street connectivity) in determining access and influencing mobility. After identifying unique groups of household agents, we design rules that utilize probability distributions generated from survey responses. The decision-making of agents that emulate households is habitual rather than utility driven. Modeled behavior is designed based on stated preferences, which may contradict premises such as the “shortest distance to the nearest shop” approach, a common assumption in the literature. We also report on three what-if scenarios to evaluate how major population changes would affect the results.
Transportation engineering, Transportation and communications
Mohammad Mehdi Oshanreh, Daniel Malarkey, Don MacKenzie
This study examines the impact of AI-based feedback and speed restrictions on reducing sidewalk riding in shared e-scooters. In partnership with Spin, 100 scooters in Santa Monica, California were fitted with computer vision, with feedback features activated on half. Data from 488 trips revealed that feedback-equipped scooters spent 22% less time and 20% less distance on sidewalks. Nearly half of riders used sidewalks for less than 10% of their trip, while around 10% spent over 60% of trip time on sidewalks, regardless of feedback. These results suggest AI feedback modifies behavior but doesn't fundamentally change diverse riding patterns.
Transportation and communications, Urban groups. The city. Urban sociology
Abstract Effective ground transportation modes linkage with the seaport plays a crucial role in facilitating smooth cargo movement from marine transportation mode to the inland areas and vice versa. Unlike road transportation, rail linkage is a cost-effective and environmentally friendly option. Inadequate sea rail connectivity within the seaport hampers cargo movement speed and impacts overall port capacity. This systematic review places emphasis on sea rail intermodal transportation at the seaport. The review categorizes and analyses previous research contributions to the sea-rail intermodal transportation system, and is organized into five categories: performance evaluation, problem-solving methodologies, planning issues, factors affecting sea rail intermodal transportation, and enhancement strategies within the context of sea rail intermodal transportation. The study discerns current research patterns and identifies gaps within the existing literature while also offering insights into potential future research avenues.
Shipment of goods. Delivery of goods, Transportation and communications
Horizontal curves of rural roads are accident-prone segments of the route. Sharp curves, steep slopes, and reduced visibility due to the mountainous environment greatly affect the driver behavior and performance. Lane-keeping ability, which is quite crucial in head-on road collisions, is a lateral driver behavior examined in a number of previous studies. This study, which is aimed to examine the naturalistic behavior, has employed the “aerial video recording” to investigate the drivers' lane-keeping ability in horizontal curves.To address the risk of encroachment (enc) into the opposite lane, this paper has developed a logistic regression model to predict the probability of a head-on collision with an enc > 0 cm threshold by exploring the relationships between road features (geometric, traffic, pavement conditions, etc.) and driver encroachment into the opposite lane. To this end, use was made of the data of 785 vehicles in 11 horizontal curves (in Kashmar-Neyshabor and Siahkal-Deylam mountainous routes) with radii in the 30–150 (m) range, deflection angles in the 80°-150° range, and slopes in the 0–8% range. The explanatory variables used in the model included the start point position (sp), road slope (Gr), sufficient stopping sight distance (Sd) and difference between the posted and vehicle speeds in mid-curves (DPS). According to the results, speeding and curve rising of 70° increased the encroachment probability, and steep upgrades exacerbated it; at a sufficient stopping sight distance, it reached 85%.
ABSTRACTThis research paper analyzes urban travel mode preference in Addis Ababa using a survey distributed to 457 public transit users. The article highlights the details of individual travel and then discusses the implications of the travel patterns for transport policy in the city. The study employed percentages and run multinomial logistic regression to analyze the relationship between mode choice, travel characteristics and socio-economic features of individuals. In terms of mode choice and the influential factors, the analysis shows that income, age, travel expenditure and distance are the determining factors that affect mode choice. It was also found that the privately owned public transport, Taxi (mini-bus) and Higer (midi-bus) form the dominant mode of transport used by closely half percent (41.3%) of the survey respondents. On the basis of our analysis, we suggest that policies have to be devised to make government owned public transport service better in order to support a shift from using low occupancy vehicles to high occupancy vehicles which ultimately solve the problem of congestion and environmental pollution.
The purpose of the optimization of holiday traffic emergency traffic organization is to solve the problem of serious traffic jams in holiday scenic spots. Based on the prediction of traffic volume and traffic mode division in the future years of the scenic spot, the traffic accident route is analyzed to provide theoretical support for the emergency traffic organization and planning of the scenic spot. This article takes the Shijiazhuang Jinta Bay scenic area as the research object, based on the traffic volume of the Jinta Bay tourist scenic area from 2009 to 2016, analyzes the traffic environment of the scenic area, predicts the traffic demand, and builds a one‐way traffic organization double‐layer optimization model. The simulated annealing algorithm is used to solve the model, an emergency transportation organization optimization plan is formulated, and the feasibility of the plan is verified through VISSIM simulation. The results of the study show that the one‐way traffic organization method reduces the average vehicle delay by 32.2% and the average queue length by 14.5%. The one‐way traffic organization based on branch diversion can more effectively solve the main road jamming and congestion caused by traffic accidents, prevent the occurrence of secondary accidents, and reduce the economic losses of scenic area managers. At the same time, the purpose of ensuring the tourist quality of tourists and the economic interests of scenic spot management departments is ensured.