Sustainable Highway Maintenance: Optimization of Existing Highway Vertical Alignment Considering Pavement Condition

Mahanpoor, Mohammad; Monajjem, Saeed; Balali, Vahid

doi:10.3390/su11061659

Cited by 14 publications

(7 citation statements)

References 21 publications

(30 reference statements)

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“…To the best of the authors' knowledge, this is the first paper to investigate the PSO algorithm for the calibration of driver behavior model parameters. Particle swarm optimization is used to develop train timetables [29], optimize the structure of short-term traffic flow prediction models [30], optimize traveler information systems [31], and to optimize the vertical alignment of roadways to reduce travel time, fuel consumption, and costs associated with the maintenance of pavements [32].…”

Section: Differentiation From Previous Literaturementioning

confidence: 99%

Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Mahmood

Kianfar

2019

Sustainability

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Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.

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Section: Differentiation From Previous Literaturementioning

confidence: 99%

Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Mahmood

Kianfar

2019

Sustainability

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“…To solve the above problems, numerous researchers have devoted considerable efforts to alignment optimization since the 1960s [1]. In the 20th century, some exact mathematical methods have been proposed, such as the calculus of variation [2] and enumeration [3].…”

Section: Introductionmentioning

confidence: 99%

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Xie

Schonfeld

et al. 2021

Sustainability

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Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation.

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“…road surface treatment, road geometry improvement, etc.) to reduce the crash risk associated with a road segment [16]. As the automobile sector is gradually moving towards complete autonomy, there is an ever-increasing requirement for scalable and cost efficient solutions to collect rich and accurate data about road networks.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to new roads, existing roads are maintained/modified for accident vulnerability prevention, traffic flow enhancement, etc. These maintenance tasks often result in change in road geometry features [16]. Typically, estimation of road transverse slope is done by extensive surveys conducted by instrumented vehicles [5,17].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones

Gupta

Khare

Jin

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Integration of information on road transverse geometric features such as cross slope and superelevation in digital maps can widen the scope of its applications, which is primarily navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS). The huge scale and dynamic nature of road networks make sensing such road geometric features a challenging task. Traditional methods oftentimes suffer from high cost, limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a costeffective and scalable road transverse slope estimation framework using sensor data from smartphones. Based on error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and GPS to estimate road transverse slope profile of a road segment. To improve accuracy and robustness of the system, the estimations of road transverse slope from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of 9km demonstrates the superior performance of our proposed method, achieving 350% improvement on road transverse slope estimation accuracy over existing methods, with 90% of errors below 0.5 •. CCS Concepts • Applied computing → Transportation; • Human-centered computing → Smartphones; • Computer systems organization → Sensors and actuators; • Hardware → Electro-mechanical devices; Digital signal processing.

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Sustainable Highway Maintenance: Optimization of Existing Highway Vertical Alignment Considering Pavement Condition

Cited by 14 publications

References 21 publications

Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones

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