Transport Network Modelling and Simulation

This chapter presents the process of building a multimodal urban net- work model using Volunteered Geographic Information (VGI) and in particular OpenStreetMap (OSM). The spatial data model design adopts a level of simplifi- cation that is adequate to OSM data availability and quality, and suitable to the measurement of the sustainable accessibility of urban neighborhoods and city- regions. The urban network model connects a private transport system (i.e. pedes- trian, bicycle, car), a public transport system (i.e. rail, metro, tram and bus) and a land use system (i.e. building land use units). Various algorithmic procedures have been developed to produce the network model, supporting the reproducibility of the process and addressing the challenges of using OSM data for this purpose. While OSM demonstrates great potential for urban analysis, thanks to the detail of its attributes and its open and universal coverage, there is still some way to go to provide the data quality and consistency required for detailed operational urban models.

Rapidly increasing congestion of traffic in urban and suburban roads raises the urgent necessity of better and quicker railway service in Sri Lanka. In studies of rail transportation planning, though, travel demand has often taken a back seat to design and engineering features; perhaps due to the lack of adequate data available.  Taking its cues, this study explores the potential of “Connectivity Analysis” to serve as an alternative methodology of travel demand forecasting.  The connectivity of railway stations in terms railway and road access were computed separately by using ‘Connectivity analysis’ and analysis the relationship with travel demand of station within the railway network of Sri Lanka. Results revealed a significant correlation between transit demand and connectivity of railway stations and connectivity values have capabilities to explain over 77% of the variation in rail transit demand. Therefore the study suggests that “Connectivity Analysis” method can serve as an alternative predictor of transit demand, in the absence of good, quality data on trip-making and employment trends.

This paper addresses freight management in large, congested urban settings. Congestion creates a substantial variation in travel time (speeds) during morning peak and evening peak periods. Besides the day-to-day dynamics of traffic conditions, traffic incidents contribute significantly to urban congestion, resulting in unplanned travel delays. Urban route designs that ignore such significant variations are often found to be inefficient or even infeasible (in regard to delivery or pickup time windows) in a congested traffic environment. This limitation may lead to delays, higher operational costs, and inferior customer service. To take the dynamics of traffic conditions into account, the proposed modeling
framework includes an underlying dynamic network traffic simulationassignment
model that is able to compute time-dependent shortest paths
(TDSPs) and simulate individual vehicles on a realistic representation
of urban road network elements, including signalization, ramp metering,
and other operational controls. A two-phase set-partitioning–based
heuristic approach is proposed to construct freight vehicle routes. No
parameter tuning is required. The integrated modeling framework
was first tested on a small network, and then the application was demonstrated on a real-world urban network, the Chicago, Illinois, area network, with problem instances of up to 500 customers with TDSPs updated at 10-min intervals.

This paper is derived from one of our research mainstream, research theme: TRAFFIC & TRANSPORTATION PSYCHOLOGY ; TRAVEL PSYCHOLOGY

Abstract:
Travel time perception and learning play a central role in the modeling of day-to-day travel choice dynamics in traffic networks and have attracted the attention of many researchers, specifically for the analysis and operation of intelligent transportation systems and travel demand management scenarios. In this paper, a fuzzy learning model is proposed to capture the mechanism by which travelers update their travel time perceptions from one day to the next, taking into account their experienced travel times. In order to capture travelers’ mental representations of uncertain travel time involving imprecision and uncertainty, a combined artificial neural network and fuzzy logic (neuro-fuzzy) architecture called Adaptive-Network-based Fuzzy Inference System (ANFIS) is employed. This framework, which utilizes a set of fuzzy if-then rules, can serve as a basis for modeling the qualitative sides of travelers’ knowledge and reasoning processes. From the output of this study, the results of our laboratory-like experiment provide a good fit to the stated data of travelers’ behavior, and may reflect the fact that the neuro-fuzzy approach can be considered a promising method in learning and perception updating models. Finally, the proposed learning model is embedded in a microscopic event based simulation framework to evaluate its credibility within a day-to-day behavior of the traffic network. The results of the simulation, which converge to the equilibrium state of the test network, are finally presented, implying that the proposed perception-updating model operates properly.

This paper presents a tool for evaluating transport layer congestion control schemes, including various TCP variants. In real computer networks, it is difficult to obtain detailed information about the internal state and operation of network protocols without disturbing protocol behavior. This is especially true for transport layer protocols where timing plays an important role. For deep analysis of protocol mechanisms, network simulation plays an essential role. This paper demonstrates a network simulator tool channel SimCast. This simulator focuses on the congestion control methods of the transport layer. Using this special purpose simulator, the effects of network topology and loss models can be investigated in detail. This paper presents a comparison of simulation results from SimCast to the output of other network simulators and real network measurements.

The paper applies the choice model incorporating attribute cut-offs proposed by . A non-compensatory choice model incorporating attribute cutoffs. Transportation Research: Part B 35 (10), 903-928] to evaluate shippers' preferences for freight service attributes. A stated preference experiment on a sample of Italian manufacturing firms shows that the marginal utilities of attributes are not constant over the whole range of values presented, and that significant improvements can be made by introducing non-linearities at points specified by individual respondents. It is found that the conventional model overestimates the importance of the attributes, though their relative ranking is maintained. The proposed choice model provides useful information on the difficulty of achieving a modal transfer of freight from road to truck-rail intermodal transport.

Distributing demand in a transportation network is based on route choice behavior models. Generally, it is assumed that drivers use routes with minimum time. In real world, drivers may consider many factors other than travel times in congested networks especially in metropolitan or two way congested transportation networks. Travel safety is a factor that one may consider in his/her trip route choice. The main objective of this paper was to investigate influence of safety factors such as crash delays on drivers' route choice behaviors. Parameters that can cause to crash occurrences were specified and their impacts were modeled at macroscopic level using a simple statistical model. Then, an equilibrium based mathematical programming model for two way networks with symmetric link interactions was proposed. The model was tested for a simple network and results showed that how crash delays can impact on route choice behaviors.

The 2-degree guardrail of global warming, together with accelerating urbanization and growing scarcity of oil, demands a redesign of todays' sprawling cities in order to limit greenhouse gases and bring about efficiently built-up structures. In 2001, 42 percent of commuter paths within Vienna are traveled by private cars. This week-daily, recurring traffic pattern causes tons of Co2 and supports low-density housing at urban fringes. In front of this background, we employ an agent-based simulation model to evaluate if commuters in Vienna who currently use motorized modes (especially car-drivers) have options to change to low-carbon transport modes (pedestrian, bicyclist, public transport) without raising costs of travel time. Using a detailed network, the identified present and alternative routes can be displayed as edge-, node-, or zonal through-traffic, highlighting differences in transport mode usage throughout the city.

The paper applies the choice model incorporating attribute cut-offs proposed by . A non-compensatory choice model incorporating attribute cutoffs. Transportation Research: Part B 35 (10), 903-928] to evaluate shippers' preferences for freight service attributes. A stated preference experiment on a sample of Italian manufacturing firms shows that the marginal utilities of attributes are not constant over the whole range of values presented, and that significant improvements can be made by introducing non-linearities at points specified by individual respondents. It is found that the conventional model overestimates the importance of the attributes, though their relative ranking is maintained. The proposed choice model provides useful information on the difficulty of achieving a modal transfer of freight from road to truck-rail intermodal transport.

This paper presents a tool for evaluating transport layer congestion control schemes, including various TCP variants. In real computer networks, it is difficult to obtain detailed information about the internal state and operation of network protocols without disturbing protocol behavior. This is especially true for transport layer protocols where timing plays an important role. For deep analysis of protocol mechanisms, network simulation plays an essential role. This paper demonstrates a network simulator tool channel SimCast. This simulator focuses on the congestion control methods of the transport layer. Using this special purpose simulator, the effects of network topology and loss models can be investigated in detail. This paper presents a comparison of simulation results from SimCast to the output of other network simulators and real network measurements.

Two family members test positive for COVID-19—how do we know who infected whom? In a perfect world, network science could provide a probable answer to such questions. [47]
This week at the 2019 American Physical Society March Meeting in Boston, Plamen Ch. Ivanov, a Boston University physicist affiliated with Harvard Medical School, will present a new paradigm called "network physiology." [46]
The Electronics and Telecommunications Research Institute (ETRI) in South Korea developed a thermoelectric module that generates electricity using human body heat. [45]
Researchers at the University of Bonn have now transferred parts of this machinery into a bacterium. [44]
Researchers in Japan have discovered that the DNA inside human cells moves around less when its genes are active. [43]
Matthias Wilmanns and colleagues at the European Molecular Biology Laboratory in Hamburg, Germany, developed methods to study the structure of a protein "strain absorber" as it changes during muscle contractions. [42]

Wireless sensor networks collect data through collaborative communication between sensor nodes. sensor nodes of wireless sensor networks are deployed in open environments. Hence, an attacker can easily compromise the node. An attacker can compromise a node to generate false reports and inject into the network. This causes unnecessary energy consumption in the process of transmitting false alarm messages and false data reports to the system. If the attacker keeps repeatedly attacking thereby causing problems such as reduction in the entire network life or disabling the networks. Yu and Guan proposed a dynamic en-route filtering scheme to detect and drop these false reports before reaching to the Base station. In the dynamic en-route filtering, the energy waste of the intermediate nodes occurs until it is detected early. In this paper, we propose a method to save the energy of the intermediate nodes by searching for the compromised node and blocking the reports generated at that node. When verifying a false report at the verification node, it can know report information. The base station is able to find the cluster of compromised nodes using that information. In particular, the base station can know the location of the node that has been compromised, we can block false alarms and energy losses by blocking reports generated in that cluster.

Maritime transport is one of the most ancient supports to human interactions across history and it still supports more than 90% of world trade volumes today. The changing connectivity of maritime networks is of crucial importance to port, transport, and economic development and planning. The way ports, terminals, but also cities, regions and countries, are connected with each other through maritime flows is not well-known and difficult to represent and measure, even for the transport actors themselves. There is a strong, urgent need for reviewing the relevant theories, concepts, methods, and sources that can be mobilized for the analysis of maritime networks.
With contributions from reputable scholars from all over the world, this book investigates the analysis of maritime flows and networks from diverse disciplinary angles going across archaeology, history, geography, regional science, economics, mathematics, physics, and computer sciences. Based on a vast array of methods, such as Geographical Information Systems (GIS), spatial analysis, complex networks, modelling, and simulation, it addresses several crucial issues related with port hierarchy; route density; modal interdependency; network robustness and vulnerability; traffic concentration and seasonality; technological change and urban/regional economic development. This book examines new evidence about how socio-economic trends are reflected (but also influenced) by maritime flows and networks, and about the way this knowledge can support and enhance decision-making in relation to the development of ports, supply chains, and transport networks in general.
This book is an ideal companion to anyone interested in the network analysis of transport systems and economic systems in general, as well as the effective ways to analyse large datasets to answer complex issues in transportation and socio-economic development.

"Under the aegis of Intelligent Transportation Systems (ITS), real-time information provision strategies are being proposed to manage traffic congestion, alleviate the effects of incidents, enhance response efficiency after disasters, and improve the multimodal/intermodal travel experience. Several simulation-based studies have been conducted in the past to analyze the evolution of the traffic network under real-time information provision to travelers†. Some of them assume specific driver behavior models and seek to understand the effect of real-time information on the unfolding network states. However, the various simulation studies are typically limited by the use of pre-specified behavior models and simple/small network topologies. Hence, there is an underlying disconnect with the real-world needs beyond the key issues related to restrictive behavioral assumptions. The proposed study seeks to address these issues by conducting experiments using a driving simulator with laboratory-based interactive experiments where the participants are provided real-time routing information on traffic conditions related to their origin-destination (O-D) trip. In this paper, we describe the survey design and methodology for the planned experiments that explicitly captures psychological factors rather than implicitly so as done in traditional models. Various variables and attributes have been identified for survey design and used in the questionnaire. The challenges associated with identification of variables, psychological factors and framing appropriate questions have been discussed. Two types of surveys are planned in this study: static and dynamic. The static survey comprises of socio-economic characteristics, travel behavior related tendencies, inherent behavioral tendencies, and attitude related characteristics. The dynamic survey is a recurring survey that captures the learning behavior of drivers based on information provision on a day-to-day basis. Hence, the study proposes to more explicitly understand the role of human behavior by incorporating appropriate elements in the experiment survey design rather than passively inferring it based on traffic measurements of the system evolution over time as is the case in simulation studies"

This article investigates the degree of overlap among the different layers of circulation composing global maritime flows in recent decades. Mobilizing several methods originating from complex networks allows us to shed new light on specialization and diversification dynamics affecting the evolution of ports and shipping. The principal confirm the strong and path-dependent influence of multiplexity on traffic volume, range of interaction and centrality from various perspectives, such as matrices correlations, homophily, assortativity, and single linkage analysis. While the network grows and concentrates around large hubs over time, traffic distribution is also place-dependent due to the reinforced position of already established nodes.

Wireless sensor networks collect data through collaborative communication between sensor nodes. sensor nodes of wireless sensor networks are deployed in open environments. Hence, an attacker can easily compromise the node. An attacker can compromise a node to generate false reports and inject into the network. This causes unnecessary energy consumption in the process of transmitting false alarm messages and false data reports to the system. If the attacker keeps repeatedly attacking thereby causing problems such as reduction in the entire network life or disabling the networks. Yu and Guan proposed a dynamic en-route filtering scheme to detect and drop these false reports before reaching to the Base station. In the dynamic en-route filtering, the energy waste of the intermediate nodes occurs until it is detected early. In this paper, we propose a method to save the energy of the intermediate nodes by searching for the compromised node and blocking the reports generated at that node. When verifying a false report at the verification node, it can know report information. The base station is able to find the cluster of compromised nodes using that information. In particular, the base station can know the location of the node that has been compromised, we can block false alarms and energy losses by blocking reports generated in that cluster.

Studies on the relation between land use and travel patterns have been subject of a series of critiques in the past (Stead and Marshall 2001; van Wee 2002), which can be in part explained by the cost of acquiring new or existing data, by the dataʼs reduced geographic coverage or detail, and by modelling and analytical constraints imposed by both software and hardware.
This project, studying the performance of TOD in the Randstad region of the Netherlands, takes advantage of recent advances in data collection and distribution and in open GIS and spatial analysis technologies to overcome some of the earlier critiques. It builds a descriptive, disaggregate, multi-modal network model of the region on the PostgreSQL / PostGIS database platform using various Open Data sources. The model integrates street network data extracted from OpenStreetMap (OSM); public transport networks data derived from the same OSM data, from the OpenOV project timetable data and from route maps in Wikipedia and the various network operators; and land use data extracted from the public Basisregister Addressen (BAG) data set of the Netherlands. The modelʼs smallest spatial units are respectively the street segments, the public transport stops and the individual buildings, and it is analysed using PostGIS, pgrouting and the R statistical platform, and the results visualised using QuantumGIS.
In this session I will be presenting some of the results, lessons learned from the process and current challenges of building such network models, focusing on issues of data quality, spatial modelling and analysis.