Innovation in Winter Road Maintenance

Umair N. Mughal, Geanette Polanco, Ingrid HowesInstitute of Industrial Technology, UiT The Arctic University of Norway, UiT Campus Narvik, 8505, Norway. Email: umair.n.mughal@uit.no, geanette.polanco@uit.no, ingrid.howes@uit.no

ABSTRACT OF RESEARCH

Road transportation is very vulnerable to climate change, especially in the Arctic region.

Even when current automobile technology has more integrated electronic sensors,

they accept sensor data as precise data. However, data can be affected by a wide

variety of failure modes of electronic sensors and the uncertainty level of the collected

information is unknown. This situation is worse in Cold Regions where low visibility, low

friction and high humidity could affect sensor functioning. The struggle to cure snow

blindness is among a number of engineering problems still to be resolved. Potential of

experiencing safety-critical events, such as, unnecessary emergency braking, inefficient

speed control, frequent use of ABS or others increases in these regions. Norwegian

Transport Agency has a 12 year traffic policy, and one of the goals is traffic safety, which

is based on zero-casualties vision. Stakeholder mapping shows that Norway has a need

for real-time road weather and condition systems. This research aims to understand

the complexity of winter road mobility under treacherous road conditions and will

suggest a support solution for viable transportation. The support system will include

off-road and on road condition monitoring sensors, with IoT at its core, to enable

rational support of winter road maintenance.

Safety and mobility winter roads are

compromised due to poor visibility and low

friction [6] and as consequence fatal collisions,

personnel injury or property damage can occur.

Mobility is highly influenced by the road surface

condition ‘RSC’, which is divided into dry, wet,

loose snow, slush, packed snow, ice, mud, loose

sand or gravel, spilled liquid and others [7].

Other aspects that contribute to ride safety and

ride comfort are road unevenness (longitudinal and

transverse) and skid resistance. Longitudinal

unevenness is related with International Roughness

Index ‘IRI’ whereas transversal unevenness is

measured through ice and snow depths, snow/ice

depth ‘SD/ID’. The skid resistance is measured

through surface friction coefficient ‘SFC”. Table 1

shows the SFC for different RSA whereas Table 2

reports SFC and accident rates ‘AR’ [9] and Table

3 summarized standards values of IRI.

Winter model developed by Arvidsson [12]

highlight the relationship between all parameters

currently considered, having Road condition

model at the centre of this model (see Figure 1).

Meanwhile road weather information system ‘RWIS’

station, IRI, SFC and SD/ID are part of the input.

An estimated the cost of basic RWIS station is

more than $50,000, without the cost of

maintenance resulting in a sparsely distribution

along the roadway. This leads to an incomplete

picture of the road surface condition.

Majority of efforts to improve the financial impact

on the road conditions and traffic flow are

oriented to enhance the quality of the input

parameters using different techniques described

as follow:

• Thermal mapping technique helps to schedule

the salt amount and rates based on RSC

information. It does not provide information

about SFC or SD/ID.

• Continuous friction measurement ‘CFM’ system

is used for RSC. CFM strongly depends upon

tyre and pavement characteristics and they

need inter-calibrations for reliable and

comparable results.

• CCTV cameras are also used by Road

maintenance agencies, but these systems

have high probability to get affected by

ambient light specially during snow events.

• Optical measurement system based on some

reflectance such as Viasala DSC111, Marwis

RWIS, Teconer RCM411, Casselgran RoadEye,

etc. are also found in practice by many road

service agencies.

NEED

Acknowledgement: This work is supported by Interreg Nord and Institute of Industrial Technology, UiT The Arctic University of Norway.

CASSELGREN

INNOVATION AB

CITIES APPLICATION

i. Increasing safety on uneven

winter road surfaces

ii. Cost effectiveness in winter

road maintenance operations

iii. Supporting autonomous winter

transportation

Keywords: Smart Mobility System; Arctic Region; Road Condition Monitoring Sensors; Internet of Things; Winter Road Maintenance

[1]. (February 12th). Smart Mobililty. Available: http://smart-transportation.org/smart-mobility/[2]. J. MacArthur, P. Mote, J. Ideker, M. Figliozzi, and M. Lee, "Climate Change Impact Assessment for SurfaceTransportation in the Pacific Northwest and Alaska," Washington State Department of Transportation2012.[3]. (2017, January 23rd). How a driveless car sees the world. Available:https://www.ted.com/talks/chris_urmson_how_a_driverless_car_sees_the_road/transcript?language=en[4]. K. Naughton. (2016, January 23rd). Driverless cars also struggle in the snow. Available:https://www.bloomberg.com/news/articles/2016-02-10/robot-cars-succumb-to-snow-blindness-as-driving-lanes-disappearhttps://www.bloomberg.com/news/articles/2016-02-10/robot-cars-succumb-to-snow-blindness-as-driving-lanes-disappear[5]. N. M. o. T. a. Communications, "National Transport Plan 2014-2023," Norwegian Ministry of Transport andCommunications 2013.[6]. F. Feng, "Winter Road Surface Condition Estimation and Forecasting," PhD, Civil Engineering, University ofWaterloo, 2013.[7]. Road Safety Research Office, "Ontario Road Safety Annual Report 2013," Ministry of Transportation, 2013.[8] "Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system,"in "White Paper," European Union, 2011[9]. C. G. Wallman and H. Astrom, "Friction measurement methods and the correlation between road friction andtraffic safety," Swedish National Road and Transport Research Institute2001.[10]. P. Mucka, "International Roughness Index specificaitons around the world," Road Materials and Pavement Design,vol. 18, no. 4, pp. 929-965, 2017.[11]. G. P. Papageorgiou and A. Mouratidis, "A mathematical approach to define threshold values of pavementcharacteristics," Structure and Infrastructure Engineering, vol. 10, no. 5, pp. 568-576, 2013.

[12]. A. K. Arvidsson, "The Winter Model - A new way to calculate socio-economic costs depending on wintermaintenance strategy," Cold Regions Science and Technology, vol. 136, pp. 30-36, 2017

It is proposed improving mobility and road

maintenance operations by the installation of

RSC monitoring sensors directly on vehicles in

combination with RWIS stations strategically

located. The system will have a backup on the

cloud. RSC will continuously report the road

surface state, even during of heavy snowfalls.

Received Information about routes will be then

collected in the local data station avoiding risk of

loss communication with the satellites.

Low Friction Low Visibility

Low Observability and Controllability could lead to serious situations

Figure 3. A Viable Solution for Winter Road Mobility

INTRODUCTION PROPOSED ACTION/PRESENT TECHNOLOGY

/A VIABLE SOLUTION FOR WINTER ROAD MOBILITY AND ROAD MAINTENANCE

/REFEERENCES

Road Surface Condition ‘RSC’ Friction Interval (SFC)

Dry bare surface 0.80-1.0

Wet, bare surface 0.70-0.80

Packed snow 0.20-0.30

Loose snow/slush 0.20-0.50

Black ice 0.15-0.30

Loose snow on black ice 0.15-0.25

Wet black ice 0.05-0.10

SFC were obtained by skiddometer measurements with 17% slip

Table 1. RSC and SFC [9]

Friction Interval (SFC) Accident Rate*

< 0.15 0.80

0.15 – 0.24 0.55

0.25 – 0.34 0.25

0.35 – 0.44 0.20

*Accident Rate is measured in personnel injuries per million vehicle km

Table 2. SFC and AR [9]

International RoughnessIndex ‘IRI’

< 1.0 1.0-1.5 1.5-2.0 > 2.0

Pavement performance Excellent Good Fair Poor

Table 3. Performance levels of IRI (m/km) [11]

Local GPS, RWIS Sources and Sinks

Local GPS, RWIS Sources and Sinks

Local GPS, RWIS Sources and Sinks

Vehicles with Integrated RSC Sensors

Vehicles with Integrated RSC Sensors

RWIS fori. Road Maintenance Agenciesii. Met Officeiii. Local Emergency Services

This data station will work as data source for

next vehicle using the same road track,

containing information about the safe routes

and tracks on a winter route until the next data

station. The vehicle will get continuous tracking

from this data station and during any unsafe

event it will automatically report to local

emergency services (hospital services, local

police agencies and local traffic control

departments). These data stations will feed

information to for road maintenance service

agencies, MET offices and traffic management

service institutions. With Internet of things ”IoT” at

its core all network components such as vehicles,

stations and cloud working space can

communicate with each other for a better and

safe driving operation. The closed loop

integration of these RSC sensors with the vehicle

sensory system will further enhance this solution

to minimize inefficient decision making during

life critical events, as the mobility become

smarter and safer during winter.

Vehicles with Integrated RSC Sensors

P R O J E C T P A R T N E R S

Figure 1. Winter Model[12]

Winter road maintenance techniques generally fall

into two categories, namely chemical and

mechanical [6]. Selection of the method to be

used depend on the information about the road

conditions. To deliver an adequate winter road

maintenance and to have a efficient winter road

plan, it is important to know both the current

and predicted road weather ‘RW’ and RSC. One

of the challenges to improve the cost

effectiveness of maintenance operations is

related to the development of new technologies

able to keep continuous track in real time of RW

and RSC under severe weather conditions during

winter.