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This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.

Recently, the serviceability of the transportation infrastructure in urban areas has become crucial. Any impact of the hazardous conditions on the urban road network causes significant disruption to the functioning of the urban region, making the city’s resilience a point of concern. Thereby, the purpose of the study is to examine the city’s recovery capacity to absorb the impacts of adverse events like urban floods.

This study examines the road network resilience for an urban flood event for zones proposed by the Municipal Corporation to develop multiple central business districts. This study proposes a novel approach to measure the resilience of road networks in an urban region under floods caused due to heavy rainfall. A novel Road Network Resilience Index (RNRI) based on the serviceability of the road network during floods is proposed, estimated using Analytic Hierarchy Process - Multiple Criteria Evaluation (AHP-MCE) approaches by using the change in street centrality, impervious area and road network density. This study examines and analyses the resilience of road networks in two conditions: flood and nonflood conditions. Resilience was estimated for both the conditions at the city level and the decentralized zone level.

Based on RNRI values, this study identifies zones having a lower or higher resilience index. The central, southern and eastern zones have lower road network resilience and western and northern zones have high road network resilience.

The proposed methodology can be used to increase road network resilience within the city under flood conditions.

The previous literature on road network resilience concentrates on the physical properties of roads after flood events. This study demonstrates the use of nonstructural measures to improve the resilience of the road network by innovatively using the AHP-MCE approach and street centrality to measure the resilience of the road network.

Road transport information systems (RTIS) and their underlying digitized road networks, have recently undergone a major enhancement with the extension from national to international databases. Today operators can choose among a wide range of strategic, tactical and operational RTIS allowing for the effective planning of international distribution. Discusses some modelling considerations behind the design and use of digitized road networks for planning and managing international distribution, in particular cross‐border or pan‐European operations. Initial emphasis is attributed to the importance of national and international road transport relative to other modes of transport. Subsequent sections highlight the benefits of international road networks and outline their area coverage. Special attention is given to the discussion of some of the network technology′s critical issues in the international (European) context. Closes by illustrating the application of digitized road networks in three state‐of‐the‐art RTIS, including vehicle routeing and scheduling software, depot allocation software and supply chain planning software.

Road transport information systems (RTISs) and their underlying digitized road networks, which were initially restricted for use in national operations only, have been enhanced and extended for use in international operations. Today operators can choose from a wide range of strategic, tactical and operational RTISs, allowing for the effective planning of international distribution. Discusses some modelling considerations behind the design and use of digitized road networks for planning and managing international distribution, in particular cross‐border or pan‐European operations. Examines the basic structure of digitized road networks and considers the benefits and problems of extending road network databases to cover several countries.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

Purpose – This chapter reviews planning and design approaches for cycle traffic in order to direct future thinking towards the critical aspects of network design that will have a beneficial impact on the utility and nature of the environment for cycling.

Approach – This chapter provides a critique of the approach of adopting a so-called hierarchy of solutions frequently adopted in western countries with low levels of cycling use.

Findings – The guiding principle for designing routes for cycle traffic is that the bicycle is a vehicle capable of speed and, as a consequence, links and junctions need to be designed according to appropriate geometric design standards. In addition, owing to the nature of the cycle and rider combination, the oft repeated Dutch characteristics for good design for cycle traffic of coherence, directness, attractiveness, safety and comfort remain firm.

Practical implications – The practical implications of the outcomes from the chapter are a method of approach for planning infrastructure for cycle traffic which starts with an analysis of demand and works through to the creation of suitable networks for cycle traffic which are grounded in, and extended from, suitably regulated existing highway networks.

Social implications – An extensive transport system suitable in nature for cycle traffic will attract a wide base of users and consequently allow for the benefits of cycling to be captured.

Value of chapter – The value of the chapter rests in its emphasis on the need to treat cycling as a distinct transport mode and, consequentially, planning and engineering needs to be undertaken in a way conducive to providing the basic necessary infrastructure for such a distinct mode.

The purpose of this study is to create a framework that integrates GIS and microscopic simulation to optimize the placement of road barriers in emergency evacuation and assess the effectiveness via simulation. Human populations are at risk from many hazards including man-made and natural disasters, sudden events that are difficult to predict and prevent, but have catastrophic consequences. A critical issue in disaster management is timely evacuation that considers the dynamic traffic demand and congestions under dynamic hazard conditions. University campuses face a unique challenge in developing effective emergency evacuation plans due to their complex site configurations, high-dense buildings and dynamic spatial-temporal distribution of population.

The framework was implemented and tested on the main campus of the Western Michigan University to optimize the placement and configuration of road barriers to achieve an effective utilization of road network when the demand way exceeds the capacity. The resulting system was also used to test the effectiveness of the phased evacuation notification strategy, both with and without the optimal road barrier strategy.

It concluded that the newly created framework and its implementation could assist emergency evacuation planning for university campuses. It also concluded that placing road barriers at appropriate locations could reduce the evacuation time by 20 per cent or more under a variety of evacuation scenarios.

The originality of this research lies in three aspects of: a university campus context, the integration of GIS and microscopic simulation in emergency evacuation and the simulation based turn restriction strategy.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.