Service Routing Research Articles

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time.

On-Demand Autonomous Vehicles in Berlin: A Cost–Benefit Analysis

The technological development of autonomous vehicles (AVs) challenges their economic relevancy. Are AVs worth it? We adapted the cost–benefit analysis framework to the case of AV services. The appraisal methodology was applied to the introduction of an on-demand AV service in Berlin, simulated through MATSim, an agent-based model. The introduction of AVs in a dense urban environment would generate more pressure on the road network. This would cause private car users to experience longer travel times, resulting in a consumer surplus loss that would not offset the new AV users’ benefits. In one configuration, a stop-based routing service appeared to generate fewer externalities (e.g., congestion, emissions, accidents) and would be cheaper to operate than door-to-door routing. Another configuration assumed that private cars were banned. In that case, the consumer surplus significantly decreased, AV services were not able to absorb the entire demand, and the shares of other modes increased. However, the externalities were significantly reduced. Finally, in an urban environment where on-demand AVs were added to the available modes, the social impact was negative for all economic agents, with heterogeneity apparent on the impacts of the different externalities. This would benefit some travelers but would also involve additional congestion. In that case, a stop-based routing seemed preferable (but still negative) to a door-to-door one. Further works discussing the introduction of AVs into an urban environment should focus on line-based options such as automated bus services.

Server Routing-Scheduling Problem in Distributed Queueing System with Time-Varying Demand and Queue Length Control

We study a server routing-scheduling problem in a distributed queueing system, where the system consists of multiple queues at different locations. In a distributed queueing system, servers are shared among multiple queues, and they travel between queues in response to stochastic and time-varying demands. Although server traveling can improve service levels and shorten queue lengths, server routing and scheduling is complicated. We propose a dynamic programming model to solve this special routing-scheduling problem with time-varying demand, stochastic travel time, and queue-length constraints. In order to tackle large-scale practical instances, we design a dynamic programming-based rollout heuristic algorithm. Experiments on large-scale airports and scenic spots show that our approach reduces the total working periods of servers/employees without violating queue-length constraints. Furthermore, we demonstrate that our algorithm outperforms existing benchmark methods and the practical schedules of a scenic spot. Funding: Financial support from the National Natural Science Foundation of China [Grant 71972133] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0099 .

Vulnerability analysis of an intercity bus carrier’s short-term operations

ABSTRACT An intercity bus service includes several bus stations for passenger access and bus service routes for bus operating between designated pairs of stations. Fundamentally, whether stations and bus service routes are operating normally or not has a direct impact on profits and the passenger service rate. However, changes in passenger demand patterns over time can lead to changes in the importance of stations and bus service routes, a problem that has rarely been discussed in the literature. This study applies a scheduling model to perform vulnerability analyses related to intercity bus schedules and routes in short-term operations. A series of analyses were conducted to observe the changes in the optimization results by changing its configurations. The results illustrate the vulnerability of the transportation system components under different scenarios and operating strategies, which could help an intercity bus carrier respond early to effectively maintain its operations.

Software Defined Network-Based Multi Service Routing Algorithm for Spatial Information Network

In order to solve the problems of unbalanced utilization of link resources due to the different capabilities of nodes in the spatial information network and the difficulty of satisfying the quality of service(QoS) with the change of task requirements, this paper proposes a multi-service routing algorithm for a spatial information network based on Software Defined Network(SDN), which divides the network transmission services into three categories: delay sensitive, bandwidth sensitive and packet loss rate sensitive. The link state information between satellite nodes is collected by an SDN controller, and then the topology-based bidirectional search algorithm is used to establish a “Source-Destination” set of routes between satellite nodes, the algorithm counts the delay, bandwidth, and packet loss rate of each route in the set, and selects routes that meet the needs of different business classes for data forwarding. The results show that the routing cost of this algorithm is lower than other algorithms, and it has better throughput performance.

Integrating train service route design with passenger flow allocation for an urban rail transit line

Train service design problem considers many operating strategies, i.e., multiple service routes, multiple train compositions, and express/local modes. Incorporating multiple operating strategies, this study first formulates an integer linear programming model for integrating train service route design with the passenger flow allocation problem. Consistency between the two components is enforced by a set of linking constraints that consider the relationship between the number of transit trips assigned to a route and the capacity of a single train. To solve the proposed model on real-life instances, we develop an approach that utilizes the Alternating Direction Method of Multipliers (ADMM). This dualizes the linking constraints and decomposes the problem into two subproblems: a train service route design subproblem and a passenger flow allocation subproblem. The latter can be subdivided into a set of passenger group-specific subproblems and is solved by a label correcting algorithm. Through Lagrangian multipliers, the interplay between the train service route design and passenger flow allocation subproblems is explored. To address the nonlinearities that arise in ADMM, we describe a new linearization technique for the quadratic penalty terms in the two subproblems by exploiting the rolling update mechanism of ADMM. The proposed approach is tested on synthetic and real-life instances from an urban rail company in China. The numerical results show that the proposed ADMM approach provides objective values that are on average 7.63% better than the conventional sequential approach. We also demonstrate that ADMM provides smaller optimality gaps in general when compared to a Lagrangian relaxation approach.

A Novel Method for Extracting and Analyzing the Geometry Properties of the Shortest Pedestrian Paths Focusing on Open Geospatial Data

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators.

FOXS-GSC—Fast Offset Xpath Service with HexagonS Communication

Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose an assistant Intelligent Transport Systems (ITS) service for traffic management in Vehicular Networks (VANET), which we name FOXS-GSC, for Fast Offset Xpath Service with hexaGonS Communication. FOXS-GSC uses a VANET communication and fog computing paradigm to detect and recommend an alternative vehicle route to avoid traffic jams. Unlike the previous solutions in the literature, the proposed service offers a versatile approach in which traffic road classification and route suggestions can be made by infrastructure or by the vehicle itself without compromising the quality of the route service. To achieve this, the service operates in a decentralized way, and the components of the service (vehicles/infrastructure) exchange messages containing vehicle information and regional traffic information. For communication, the proposed approach uses a new dedicated multi-hop protocol that has been specifically designed based on the characteristics and requirements of a vehicle routing service. Therefore, by adapting to the inherent characteristics of a vehicle routing service, such as the density of regions, the proposed communication protocol both enhances reliability and improves the overall efficiency of the vehicle routing service. Simulation results comparing FOXS-GSC with baseline solutions and other proposals from the literature demonstrate its significant impact, reducing network congestion by up to 95% while maintaining a coverage of 97% across various scenery characteristics. Concerning road traffic efficiency, the traffic quality is increasing by 29%, for a reduction in carbon emissions of 10%.

Nationwide Demand Modeling for an Urban Air Mobility Commuting Mission

In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using the implemented model, we identify New York City, San Francisco, and Los Angeles as cities with the highest potential commuter demand, and Seattle as the city most resilient to increases in delay time. A sensitivity study of demand is performed and shows that strong demand exists for short trips with low total delay times and for longer trips with a low ticket price per kilometer, with the former showing resilience to increases in operational costs and the latter showing resilience to increases in delays. The demand model is supported by a speed-flow model, which fuses highway performance monitoring system data with OpenStreetMap data to provide traffic-adjusted road segment speeds to OSRM. The speed-flow model has the capability of providing congestion data for road segments across the United States without the use of commercial data sets or routing services and is shown to improve routing duration accuracy in congested regions.

Self-Adaptive Discrete Cuckoo Search Algorithm for the Service Routing Problem with Time Windows and Stochastic Service Time

Self-Adaptive Discrete Cuckoo Search Algorithm for the Service Routing Problem with Time Windows and Stochastic Service Time

Emission Control Routes in Container Shipping between Korea-China

Purpose – As the severity of air pollution caused by the shipping industry is becoming evident, port authorities have started making efforts to reduce air pollutants. Considering the limitations of the currently implemented emission-control area (ECA) and vessel-speed reduction program (VSRP), which are narrow in the designation range and navigation behavior of ships, this study proposes an emission-control route (ECR) that can complement the aforementioned two environmental policies. Design/methodology – This study was conducted on Korea-China trade service routes (ports of call) of regular liners. This study employed vessel-specific data, which is from an automatic identification system (AIS), for 1,728 maritime transportations performed by 387 container vessels during one year (July 1, 2021, to June 30, 2022). Performing a scenario analysis, this study analyzed the effectiveness of reduced air-pollutant emissions. Findings – This study found that the implementation of ECRs could increase average voyage time by 12.38%-25.28% but reduced air-pollutant emissions by 29.02%-43.54%. Additionally, the increase in average voyage times reduces the anchorage time of ships outside ports, providing an incentive for ship operators to voluntarily participate in compliance with regulations, thereby contributing to the establishment of a virtuous cycle of air-environmental policies related to ships. Originality/value – This study aims to verify the policy effectiveness by designing an ECR scope for liner trade routes between Korea and China. Therefore, originality and the value of this study includes conceptualizing the ECR system, analyzing its environmental performance, and exploring new policies that can be implemented while complementing existing policies.

ПРІОРИТЕТНІ ЗАДАЧІ СМАРТИЗАЦІЇ ШОПІНГУ У ТРАДИЦІЙНОМУ ГІПЕРМАРКЕТІ ЗА ПРИНЦИПАМИ RETAIL 4.0

Retail 4.0 is the newest approach to Industry 4.0-based retail organization. Industry 4.0 involves the industry transformation to the most robotic production, which is controlled by intelligent systems based on cyber-physical devices with usage of Internet of things, cloud information resources, big data processing, and virtual reality. The paper proposes two promising tasks for the implementation of Retail 4.0 technologies in the practices of traditional hypermarkets and supermarkets with low investments. The first task concerns the optimization of the buyer's route, and the second task concerns the optimization of the route of the store employee who completes the online order. It is assumed that the trolleys with RFID-modules and a system of radio frequency identification of the location of devices are available in a supermarket. Task of routine optimization for a buyer with a smart trolley is reduced to a generalized traveling salesman task. The possibility of dynamic adaptation of the route due to changes in the availability of aisles and stock of goods is considered. Three monetization ways of the proposed routine optimization service are proposed. They are based either on direct payment by the buyer for optimal routing service, or indirect payment through increased loyalty or the ability to promote additional products through the recommendation module. Recommendations are provided at hand, i.e. directly in the trading zone near the area of visibility to the recommending product. The second task concerns the optimal completion of online orders by store employees. In addition to the possibility of dynamic adaptation of the route, to save time, it is additionally suggested to complete two similar small orders at the same time. The principles by which it is possible to identify the most similar pairs of orders from the point of view of their picking routes have been formulated.

TRAFFIC SPEED MODELLING TO IMPROVE TRAVEL TIME ESTIMATION IN OPENROUTESERVICE

Abstract. Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.

Charging Station Location and Sizing for Electric Vehicles Under Congestion

This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .

Securing AMI-IoT networks against multiple RPL attacks using ensemble learning IDS and light-chain based prediction detection and mitigation mechanisms

ABSTRACT Advanced Metering Infrastructure (AMI) is one of the Internet of Things (IoT) enabled smart applications of smart grids. The Routing Protocol for Low Power and Lossy network (RPL) has been accepted to facilitate effective routing services for the AMI. However, numerous RPL attacks appear in AMI due to resource scarcity and dubious wireless medium, which significantly impedes the successful deployment of AMI-RPL. To enable secure and reliable AMI-RPL, this work proposes a novel Intrusion Detection System (IDS) named AMI Attack-aware Intelligent Machine learning IDS (AIMS). The primary objectives of AIMS are to predict, detect, and mitigate different types of RPL security attacks in the AMI environment. To predict the RPL attacks using the Stacked Ensemble (SE) machine learning model, a novel AMI-RPL Attack Dataset (ARAD) is generated by the Cooja simulator with the suitable pre-processing and the Spider Monkey Optimization (SMO) based feature selection. The advanced prediction of attack nodes improves the performance and significantly diminishes the future damages of AMI. The attack detection is based on immutable blocks of a light-chain model, and the cryptocurrency-based mitigation model effectively isolates the attackers. AIMS mechanism amplifies RPL security with high reliability and maximizes the AMI network lifetime by delivering superior results.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago’s “L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods’ to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that “L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users’ risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods’ access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling.

Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely considered in the rolling stock scheduling. An integrated optimization solution is proposed which considers the line plan, timetable, and rolling stock schedule. Candidate service routes are generated according to the layout of the turn-back stations. Considering the constraints of operation and passenger flow demand, an integer nonlinear programming model is established to minimize the cost of operation and passenger waiting time. The model complexity is analyzed and based on its decomposability a deterministic search algorithm is designed. Taking Chongqing Metro Line 3 in China as an example to verify the effectiveness of the proposed model and algorithm. Compared with the train operation plan based on manual experience and compiled by stages, the integrated optimization model can better improve the quality of train operation plan.

“Mom Calling the Commanding Officer”: The Changing Relationship Between Mothers and Their Sons Serving in Israel Defense Forces

Anchored in feminist theory, this article examines the relationship between mothers and their sons serving in Israel Defense Forces. A review of the feminist literature reveals that the military is one of the main sources of gender inequality and reproduction of traditional gender roles. The military plays a pivotal role in male socialization and encourages soldiers to distance themselves both physically and psychologically from their mothers and deny any feminine traits that may be attributed to them. Yet this qualitative study, which is based on 28 interviews with mothers and sons, reveals a more complex picture. Although the military does serve as a gatekeeper that distances mothers and reinforces hypermasculinized culture, the participants depict the mothers’ active involvement in the daily life of their soldier sons without any sense of inferiority in confronting the military apparatus. The mothers assume the role of psychologists, save the sons from entanglement with their direct commanders, and even organize their sons’ service route. The extension of maternal practices into the military realm blurs the binary conceptualization of “men in arms and women at home” and sheds more light on contemporary changes that have taken place in military–family relations in Israel.

SDBGPChain: A decentralized low complexity framework to detect and prevent the BGPattacks using SDN with smart contract based Dendrimer tree blockchain

The Border Gateway Protocol (BGP) helps to exchange the information over the internet. However, BGP is susceptible to malicious attacks and misconfigurations meanwhile BGP does not focus on providing security during routing information advertisement. The secure BGP environment provides constant routing services on the internet, but existing works does not provide an optimal solution for BGP router security. To overcome these issues, we proposed decentralized low complexity framework for providing security to BGP attacks using deep learning and blockchain technologies. The proposed SDBGPChain (SDN based BGP using dendrimer tree blockchain) model whereas the SDN provides network management, flexibility and scalability for BGP environment, the proposed work includes three processes such as entity authentication, low complexity BGP attack detection and prevention, and deep learning-based route update validation. Initially, users and BGP routers are authenticated using Enhanced Blowfish and Elliptical Curve Cryptography (EB-ECC) which generated the unique key using EB algorithm and the unique is encrypted by enhanced ECC for improving security. Every Autonomous System (AS) is constructed as a graph structure using Directed Acyclic Graph (DAG) which improves the scalability and interoperability of the network. Secondly, we perform BGP attack detection using Dual Agent based Twin Actor Twin Delayed Deep Deterministic Policy Gradient (DA-T2TD3) algorithm by performing both proactive and reactive methods. Finally, we perform route update validation by Gated Recurrent Unit (GRU) by considering update message tuples which validate the updates by identifying misconfigurations and errors in the updated messages. Here, updated messages are passed to the blockchain for validation using the DiemBFT consensus algorithm which improves high security. The simulation of the proposed SDBGPChain model is conducted by OMNET++ simulation tool, and the performance of this work is evaluated based on various performance metrics such as Packet drop ratio (0.22), Throughput (39.8 bits/sec), Block validation time (20.2 s), Detection accuracy ((no. of attacks 92.5%) and (no of anomalies 93%)), and Network traffic (27.6%) which proved that the proposed work achieved superior performance compared to state-of-the-art works.