Scholarly article on topic 'Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms of Fuel Consumption'

Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms of Fuel Consumption Academic research paper on "Economics and business"

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Procedia Engineering
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{"Intelligent Transportation Systems for Commercial Vehicle Operations" / "mobile communication" / "Vehicular ad-hoc network" / "pollutants emissions"}

Abstract of research paper on Economics and business, author of scientific article — Ion Nicolae Stancel, Maria Claudia Surugiu

Abstract Intelligent Transport Systems applications for commercial vehicles are aimed at minimizing stops that are not strictly necessary (for weight control, licenses or approvals) and improved logistics for fleet operators, including the use of intermodal transport. This article addresses the development of a system that monitors and informs a road train, that forms in real-time, about the length of a selected route, the travel time on the route, and especially the route fuel consumption. By collecting information from fleets of trucks, the communication interface between the vehicle and the fleet monitoring and information system will cover a large area of interest. The system will receive data from the vehicles and after a specific processing stage, will display optimal routes in terms of fuel consumption.

Academic research paper on topic "Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms of Fuel Consumption"

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Procedía Engineering 181 (2017) 861 - 867

Procedía Engineering

www.elsevier.com/locate/procedia

10th International Conference Interdisciplinarity in Engineering, INTER-ENG 2016

Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms ofFuel Consumption

Ion Nicolae Stancela, Maria Claudia Suragiua'*

aUniversity Politehnica of Bucharest, Transport Faculty, Remote Control and Electronics in Transports Department, 313, Splaiul Independentei,

JE 008, RO 060042, Bucharest, Romania

Abstract

Intelligent Transport Systems applications for commercial vehicles are aimed at minimizing stops that are not strictly necessary (for weight control, licenses or approvals) and improved logistics for fleet operators, including the use of intermodal transport. This article addresses the development of a system that monitors and informs a road train, that forms in real-time, about the length of a selected route, the travel time on the route, and especially the route fuel consumption. By collecting information from fleets of trucks, the communication interface between the vehicle and the fleet monitoring and information system will cover a large area of interest. The system will receive data from the vehicles and after a specific processing stage, will display optimal routes in terms of fuel consumption.

©2017 The Authors.PublishedbyElsevierLtd. Thisis an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of INTER-ENG 2016

Keywords: Intelligent Transportation Systems for Commercial Vehicle Operations; mobile communication; Vehicular ad-hoc network; pollutants emissions.

1. Introduction

The development of Intelligent Transport Systems (ITS) requires real-time and high-quality traffic information. Over the past few years, under increasing pressure to improve traffic management, traffic data collection methods have evolved considerably and access to real-time information has become routine over the world.

* Corresponding author. Tel.: +40-740-029048. E-mail address: claudia.surugiu@upb.ro

1877-7058 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of INTER-ENG 2016

doi: 10.1016/j.proeng.2017.02.478

Intelligent Transport Systems applications for commercial vehicles are aimed at minimizing stops that are not strictly necessary (for weight control, licenses or approvals) and improve logistics for fleet operators, including the use of intermodal transport [1]. Using traditional sensors "on the road" (i.e., inductive loops) for data collection is necessary, but not sufficient, due to their limited coverage and high costs of implementation and maintenance. In recent years we have witnessed the emergence of alternative sources for data collection [2]. An example in this case is the method based on vehicle location (Floating Car Data) [2, 3], which promises to be a better solution in terms of cost-effectiveness to supplement the limitations of fixed detectors. Even if the idea of collecting data through On Board Units (OBU), cellular networks or GPS is not exactly new, the FCD market (Floating Car Data) is growing worldwide with a wide range of applications and benefits [2, 4, 5].

Since the article presents a model of efficiency in fuel consumption and emissions reduction for truck platoons, it is worth mentioning about commercial vehicle operations. The technologies used in CVO (Commercial Vehicle Operations) include: labelling systems and automatic tracking of the movement of hazardous materials and automatic collection of taxes on roads whose access is chargeable and automatic vehicle identification, to grant the vehicle all the approvals necessary to pass easily through checkpoints on the route and automatic. All vehicles moving in platoon are connected [6]. While driving the first truck is the leader and indicates the speed and direction of travel. Through a V2V (Vehicle to Vehicle) communication link, the necessary control commands reach the next vehicles in a data form and the trucks send back the information to the first vehicle. A wireless LAN connection is used between trucks, to exchange data. By moving in a convoy, an improvement in traffic safety is desired [6]. About 90% of road accidents are caused by human error.

2. Research methodology

Each truck which will take part of this monitoring and information network will provide information on consumption, speed, mass to a central server in order to obtain an optimal view of the routes. Thus, through this "map", one or more trucks can be part of a road train already created. Field data will be transmitted via mobile communications, used at vehicle level [6]. With the surge in the number of cars per capita, the infrastructure has not adapted to those needs, so the intelligent transport systems are required in order to maximize the available road infrastructure; by organizing and integrating trucks into road-trains, the amount of pollutants emissions will be reduced, fuel costs will decrease and the lap times of routes will shorten.

The principle of FCD (Floating Car Data) is to collect real-time data about traffic [5], locating the vehicle, via mobile phones or GPS over the entire road network. This means, in essence, that each vehicle is equipped with GPS or mobile phone, which acts as a sensor for the road network [7]. Data such as vehicle location, speed and direction are sent anonymously to a central processing centre. After being collected and extracted relevant information (eg, status of traffic, alternative routes) can be redistributed to drivers out on the road. This technology is an alternative or rather a complementary source of high quality data on existing technologies. Technology will help improve safety, efficiency and reliability of the transmission system.

FCD technology is based on data from "probes" represented by cellular and GPS devices [2,4]. This is one of the categories within the family of mobile traffic probes. The other category of data collection "in-vehicle" refers to Automatic Vehicle Identification (AVI) [4]. In this method, information for vehicles is taken from fixed points by means of electronic tags that are read as vehicles pass by the sensors. Basically, there are two main types of FCD (Floating Car Data), namely GPS devices and cellular based systems (mobile) [4].

2.1. Emission Evaluation

This paper presents the COPERT (COmputer Programme to calculate Emissions from Road Transport) III (estimating road transport emissions) methodology which represents the latest and improved version [8]. COPERT calculates emissions as a product of activity data (i.e. mileage) and speed-dependent realworld [9] emission factors.

The methodology based on COPERT 4 shows these significant changes compared [10] to the one from 2000: • The list of categories of vehicles has been revised, including a number of weight classes for heavy freight cars

and taking into account separately, buses and coaches.

• Factors related to emissions in terms of speed and fuel consumption were introduced for all vehicles (except engines with capacity ofless than 50 cm3). Also, a specific reduction factor for each pollutant was introduced who takes into account future and improved technologies and the calculated emission factor that applies the best available technology appropriately.

• Calculation of emissions from heavy cargo vehicles is improved by considering the gradient of the road and weight carried.

Simple methodology for application is necessary to know each category of vehicles either total fuel consumption or the number of vehicles per category and length of the route.

• source fits in group activities / appropriate sources;

• identifies pollutant emission factors per km per kg of fuel for different categories of vehicles - from these tables both necessary basic data as well as emission factors needed for emission calculations are extracted.

Option 1

• path length;

• traffic intensity - number of vehicles per category in a time;

• specific consumption (kg /100 km) of fuel on fuel types and vehicles categories;

• TEPb (tetratecs oflead) and S(sulfur) content of fuel. Option 2

• path length;

• The total fuel consumption (kg) of fuel types and vehicle categories in a time frame for going through the entire route;

• TEPb and S content of fuels. Option 3

• path length;

• traffic intensity - number vehicles per categories, in a time period.

Results obtained due to applying this methodology include: qualify the source in the classification of European standards with pollution source identification; total mass flow of pollutants emitted by the entire traffic route; pollutant mass flow per unit length.

The method of calculating emissions of pollutants according to SNAP code for transports [11], commercial vehicles, is determined by equation (1):

Ep Q,m * (!)

where:

Ep = emission of pollutant p [g],

Ci m = fuel consumption of vehicle category j using fuel m [kg],

CSp,i,m = fuel consumption-specific emission factor of pollutant i for vehicle category i and fuel m [g/kg] [11].

It is advisable that when it comes to determining emissions from road traffic using the simple methodology CORINAIR in the process of collecting data to focus on knowing the consumption of fuels (appropriate emission factors have a greater degree of confidence), and as detailed knowledge of vehicle categories and the number of vehicles in each category.

To determine the amount of emissions they take into account the vehicles density reported to the distance between trucks, the length and the distance between platoons consecutive. It is determined by [12] based on the relationship (2).

The vehicle density for an automated lane is given as:

dpi+n{L+dtr)—dtr

where:

dtr = intratruck spacing

dpj = interplatoon spacing L = length of vehicle n = number of cars in platoon

3. Results and contributions

In this paper, the following aspects are listed: reducing the cost of road transportation by organizing trucks into optimal road-trains in terms of fuel consumption, thus optimizing traffic flow.

The contribution of the paper refers is the implementation of an algorithm to optimize of routes for commercial vehicles. These will be grouped by type of freight, destination and distance. In the grouping, each participant truck will become a wireless router, which will give information for the truck in close proximity. Each truck will be equipped with a GPS navigation system and when communication is needed globally, it can call on the mobile communication network (cellular) [1, 2,6].

In this section, the network topology of the proposed model, the traffic information reporting flow, and the neighbouring vehicle detection and truck platoon linker flow are described. The RSU is used as a wireless LAN access point, in VANET communications, in Fig. 1.

Fig. 1. Network topology oftruck platooning route selection algorithm

It enables vehicles to exchange data and information with other vehicles or devices within its transmission range. The information of this proposed model is an electronic road database consisting of information gathered from trucks, not necessarily organized in a platoon manner [1, 7]. As the trucks, which are connected to the network, run multiple routes, the electronic road map is updated with information regarding the truck's average speed (maximum

speed, minimum speed, number of stops), fuel consumption as showed in Fig. 2a. Based on these data, we can infer C02 emissions and NOx.

At this moment, any truck that passes into a wireless communication range of an RSU, or, if it's not in that range, it sends an interrogation message through LTE connectivity to the traffic monitoring centre. The received message will consist of data about witch route will be more fuel economic and less pollutant. To ensure these characteristics, a platoon can be formed based on these data [12]. As another truck enters the range of the first truck, both of them having the same direction of travel, the VANET traffic information distribution algorithm is activated as presented in Fig. 2b. The initial truck interrogates its new neighbour about its route. If it's a common route, the two trucks engage in a truck platoon formation. From this moment road train will still transmit field data about fuel consumption and emissions to the traffic monitoring centre. At the same time, the new local area network (LAN) formed between the two trucks will be able to receive further other trucks with same route, but up to a certain number, to avoid causing traffic congestions or delays [5], [14].

In Fig. 3a and Fig. 3b are represented average fuel consumption for different scenarios to consider as (shooting, platoon the distance between trucks of 2 or 3 meters) depending on the speed of movement [13, 14]. Also can be seen as a reduction of the pollutant emissions (NOx exemplified).

It can be seen a decrease in NOx emissions when the trucks are formed in a platoon with distance between them 2 or 3 meters. Platoon running at a constant speed of about 90km/h the quantity of pollutants decreases as fuel consumption.

Record truck

travel speed

Determine the

average travel

speed of the

truck on the

monitored road

segment

Generate a

traffic

information

report for the

considered road

segment

Engages ÜPÜ

trai m

Updated report with information provided from the road train (position, travel direction, average speed, average fuel consumption)

Updated report is transmitted to the traffic monitoring center

generated by road train

Continue own route

Fig. 2. (a) Traffic information recording flow of the truck; (b) Traffic information and road train management flow.

(a) (b)

Fig. 3. (a) Variation in fuel consumption depending on the average speed ofmovement ofa truck and a platoon with a distance between trucks with 2m or 3 m; (b) Variation pollution emissions (NOx) depending on the average speed ofmovement ofatruck and a platoon with a distance between trucks with 2m or 3m.

4. Conclusions

An interconnected road train platoon increases traffic safety, the road infrastructure is not affected by the application of this management system for trucks and comes as a solution for drivers with long routes.

A platoon or road train means an interconnected system of vehicles for road traffic, especially commercial transport, which is still in a development stage, with two or more trucks, which, with the help of guidance systems for travellers and control and communications V2V (vehicle to vehicle), moving a short distance behind each other without affecting safety. The minimum distance between trucks be considered, it is 2 meters and can be extended up to 18 meters according to European standards, respectively, half a second between them. The main objective of this study is that the route thus created will bring fuel savings of up to 10%, calculated for the entire platoon. Besides fuel economy of trucks running in platoon formation connected a reduction of C02 and NOx emissions are obtained. These effects are expected at an average speed of 80 km/h.

Assuring a communication between different trucks through intelligent communication technologies (VANET or cellular) leads to increased safety for truck drivers and road traffic on highways. Benefits compositions in platoons of trucks are:

• Optimization of road capacity (reducing congestion on highway),

• Safety: Platooning asset utilization optimization technology will be introduced and accepted by society if it is (almost) only fail-safe,

• Drastic decrease in accidents,

• Low fuel consumption,

• Reducing emissions.

In the next stages of international research a present knowledge will be built to tackle the challenges of automating merging, lane changing, as well as joining and leaving the platoon. Methods must be developed, so that truck caravans in the right lane do not hinder merging for other motorists or present a barrier to general traffic flow. Other drivers must be made aware of the truck convoys in a timely and safe manner, and to stay out of them, so on-and off-road signs/indicators and local V2V broadcasts will be necessary.

References

[1] M.C. Surugiu, I.N. Stancel, Fleet management cooperative Systems for commercial vehicles, in: 9th International Conference Interdisciplinarity in Engineering, INTER-ENG 2015, 8-9 October 2015, Tlrgu-Mures, Romania, Procedía Technology, Volume 22, 2016, pp. 984-990.

[2] S. Eichler, Anonymous and Authenticated Data Provisioning for Floating Car Data Systems, in: Proceedings of the 10th IEEE International Conference on Communication Systems (ICCS), 2006.

[3] S. Fraser, The use of floating cellular telephone data for real-time transportation incident management, McMaster University, School of Engineering Practice, 2007.

[4] G. Leduc, Road Traffic Data: Collection Methods and Applications, European Communities, 2008.

[5] Travel Time Information Using Cell Phones (TTECP) for Highways and Roadways, Department of Electrical and Computer Engineering, FIU, Final Report prepared for the Florida Department of Transportation, 2007.

[6] R. V. Gheorghiu, V. A. Stan, On the communication network inside vehicles, in: 7th International Conference on Electronics, Computers and Artificial Intelligence - ECAI 2015, June 25 - June 27, 2015.

[7] J. Ploeg, A. F.A. Serrarens, G. J. Heijenk, Connect & Drive: design and evaluation ofcooperative adaptive cruise control for congestion reduction, Journal of Modern Transportation 19(3) (2011) 207-213.

[8] EEA -Technical Report no. 50, 2000.

[9] *** EEA , Explaining road transport emissions, A non-technical guide, European Environment Agency, Copenhagen, 2016.

[10] M. Kousoulidou, L. Ntziachristos, S. Gkeivanidis, Z. Samaras, Validation ofthe COPERT road emission inventory model with real-use data, in: 19th International Emission Inventory Conference Emission Inventories - Informing Emerging Issues, 2010.

[11] ***SNAP CODES, ROAD TRANSPORT, rt070100 Activities 070100 - 070500.

[12] J. B. Matthew, J. M. Norbeck, TransportationModeling for the Environment: Final Report, 1996.

[13] W.K. Wolterink, G.J. Heijenk, G. Karagiannis, Constrained geocast to support cooperative adaptive cruise control (CACC) merging, in: Proceedings ofthe Second IEEE Vehicular Networking Conference (VNC 2010), Jersey City, New Jersey, 2010, pp. 41-48.

[14] J. B. Matthew, T. Younglove, G. Scora, Development of a Heavy-Duty Diesel Modal Emissions and Fuel Consumption, California PATH Research Report.