Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network

State estimation in urban traffic networks: A two-layer approach

PurposeSingapore's land transportation is governed by a comprehensive set of rules, regulations, operations and systems to ensure its effectiveness and efficiency. The Land Transport Authority (LTA) is the main agency in charge of road operations. Despite the LTA's best efforts, road related problems still surface every now and then. The purpose of this paper is to investigate how to boost the performance of existing road infrastructure. Using Singapore as a case, the specific research objectives are to: ascertain motorists’ level of satisfaction with the road infrastructure; find out problems faced by motorists when using the road infrastructure; determine factors affecting motorists’ satisfaction with the road infrastructure; and design and test a model to predict motorists’ satisfaction with the road infrastructure.Design/methodology/approachA questionnaire survey was conducted on motorists. Through a combination of convenience sampling and snowball sampling, a total of 53 responses were collected. After the survey, interviews were conducted with subject matter experts to understand the current practices and possible solutions to the problems.FindingsThe most significant problem is water ponding on roads. Traffic redirection due to road works and coordination among government agencies in road opening works also did not achieve significantly good ratings. Two practices are found to directly give rise to higher satisfaction with the road infrastructure: road cleanliness; and efficiency in traffic redirection arising from road works.Research limitations/implicationsMotorists were asked to rate on a ten‐point scale. This may give rise to bias because satisfaction and performance are subjective and not easily quantified.Practical implicationsMotorists’ satisfaction level with the road infrastructure is dependent on cleanliness of roads and efficiency of traffic diversion when there are road works. The implication is that the government should arrange for roads to be regularly cleaned and at the same time educate the public to keep roads clean and enforce laws against those who litter. The time for traffic diversion should be minimized by increasing productivity of road works, so that these can be completed quickly.Originality/valueThe road network is a valuable infrastructural asset of a country. Due to its large scale and complexity, managing road operations is no easy feat. Even though Singapore has a comprehensive set of procedures, rules and regulations to manage this asset, problems and complaints continue to surface. This study has identified the major problems plaguing Singapore's road infrastructure. Furthermore, a model to predict motorists’ satisfaction with the road infrastructure was successfully developed and tested. With this knowledge in mind, the LTA can focus on improving these factors to boost performance of road infrastructure through road maintenance and road works.

In the past, when assessing the stability of roadside slopes in mountainous areas of Taiwan, road maintenance and inspection personnel were often limited by steep terrain and vegetation cover. They could only observe damages on the road surface and sides instead of grasping the key points of landslides and disasters. Among them, Sandimen and Wutai located on Highway 24 in Pingtung repeatedly suffer from natural disasters, such as falling rocks, debris avalanche or subsidence of roadbed, after the strike of Typhoon Morakot whenever a typhoon, torrential rain or earthquake event occurs. The government therefore spent a lot of money on road maintenance works. In this area, we have combined topographic features identified from LiDAR-derived 1-m resolution DEM and large-scale ground surface deformation observed using the multitemporal InSAR technique (MT-InSAR) developed based on ALOS-2 / PALSAR-2 images collected between 2015 and 2022. Then Sections 29.5K and 34.5K are selected as the key study area of this investigation. The cumulative deformation results of these key slopes are: -80 mm at 29.5K from ALOS-2 imagery and -103 mm at 34.5K from ALOS-2. As for the section of 29.5K, from May 8, 2016 to December 4, 2016, the overall slope of the road was affected by several heavy rains and typhoons, among which the maximum rainfall on that day reached 303 mm; and the amount of deformation decreased by 16.7 mm; the average deformation of subarea block A decreased by 33.6 mm, and the average deformation of subarea block B decreased by 14.8 mm. As for the section of 34.5K, the overall roadside slopes were affected by several heavy rains and typhoons, among which the maximum accumulation of rainfall on that day reached 303 mm, from May 8, 2016 to December 4, 2016. The amount of deformation decreased by 13.7 mm. Our primary results demonstrate that the cumulative deformation and rainfall of these two key slopes show a positive correlation.

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision.

ABSTRACTWork zones for road maintenance in traffic networks can significantly impact the traffic distribution and route choice behaviour of travellers. This study proposes an approach to evaluate and predict the broad‐scale effects of work zones on large‐scale traffic networks. For the requirement of the efficient evaluation of the various impacts of work zones on traffic networks, this study defines the road maintenance sensitivity factor (RMSF) to represent the joint impact of work zones. A simulation‐based optimization method for calibrating the RMSF is formulated. The original objective function is replaced by an analytical metamodel that builds the approximate relationship between the RMSFs and traffic flow distribution with the effect of work zones. A derivative‐free trust‐region algorithm is used to obtain the optimal solution. Numerical experiments are conducted on a small test network and a large‐scale freeway network. The proposed method shows the accuracy and effectiveness with tight computational resources than the simultaneous perturbation stochastic approximation method in both experiments, giving the RMSF results and map the traffic redistribution of large‐scale networks with work zones accurately and efficiently, which can help traffic managers to optimize maintenance plans and traffic management measures with the assistance of the traffic management system.

This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable.

Road construction and maintenance is one of the largest government administered construction programs in Hong Kong. The large scale of the road construction and maintenance work has significant implication on economic, environment and society. A research question that should be emphasized by the administrators in Hong Kong is: what policy should be framed road construction and maintenance can achieve the most beneficial. This study aims to lay out a roadmap to the sustainable road construction and maintenance in urban environment based on Hong Kong case study. Four basic components were identified such as criteria, data, models and optimization. According to each component, several methods were explained to address the research questions. It is hoped that the present research has made a contribution to assist policy making for the road administers.

Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model

New Approach to Modelling and its Application in Transportation in Urban Traffic

A road network is a way of transporting, sharing and transporting goods and services locally to the community. Road networks are also the means of communication in some parts of the world. Therefore, access to good roads improves the quality of life and work of the people living in the community. But the poor condition of the design and development of the road network coupled with natural disasters such as heavy rains have caused many unwanted potholes and abrasions on the roads which are very dangerous for commuters and other road users and for road users. In addition, the lack of a proper road maintenance program has resulted in a steady increase in the number of potholes that endanger transport and road safety. Meanwhile road maintenance works are heavily dependent on actual acquisition and reporting.

Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades. Its applications in land-based urban traffic network studies have been fruitful, but have suffered from the lack of a systematic cognitive and integration framework. This paper reviews complex network theory related knowledge and discusses its applications in urban traffic network studies in several directions. This includes network representation methods, topological and geographical related studies, network communities mining, network robustness and vulnerability, big-data-based research, network optimization, co-evolution research and multilayer network theory related studies. Finally, new research directions are pointed out. With these efforts, this physics-based concept will be more easily and widely accepted by urban traffic network planners, designers, and other related scholars.

The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.

A dynamic route optimization scheme based on short-term traffic flow prediction is designed. The overall idea of this paper is to use the real-time information of each section of the urban road communication network to predict the short-term traffic speed of the road in the next 5 min, in order to reflect the changes of the road traffic state of each section of the urban road traffic network. Then, the speed prediction information of each section in the urban road traffic network is converted into the estimated average travel time of each section by using the speed-time conversion formula, and then the road weight of each section in the urban road traffic network is set. Finally, the optimal route at the current time is calculated by using the improved algorithm based on the traditional Dijkstra algorithm. Before the traffic participant arrives at the destination, the scheme will cycle through the short-term prediction and route optimization of the traffic flow until the traffic participant arrives at the destination. And then achieve the balance of traffic conditions of various sections in the urban road traffic network, improve the situation of urban road traffic congestion, and avoid the emergence of bad road traffic conditions such as “navigation congestion”.

An Efficient Model for Urban Traffic Network Control

The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from 40 road sections managed by the city’s road maintenance division. Variables used included Composition, Condition, Depression, Patches, Damage types, Crack Area, and Crack Width. The results demonstrated high accuracy in predicting SDI, with model performance achieving an R² of 0.87. This model can be applied to optimize the efficiency of road maintenance strategies.