Managing an Urban Transport System in Enhancing the Area

Stability

Irina Makarova, Rifat Khabibullin, Eduard Belyaev and Vadim Mavrin

Kazan Federal University, Suyumbike, 10A, 423800, Naberezhnye Chelny, Russian Federation

Keywords: Air, Maximum Allowable Concentration, Road Transport, Intensity, Motorway.

Abstract: The urbanized mankind is faced with vulnerability of urban systems, migration and concentration of

population, low quality of habitat, loss of fertile land, and necessity of waste disposal. In large cities, a

significant contribution to atmospheric pollution with sulphur dioxide, nitrogen and carbon oxides, and

industrial dust comes from the motor transport. The motor traffic growth inevitably affects the human health

by causing road and transport traumatism, respiratory diseases and diseases caused by physical inactivity.

The proposed solution is based on optimization of a city transport system parameters. This was achieved by

via simulation modelling taking into account a large number of parameters, both within and outside the

system, many of the latter being stochastic. The recommendations include rearranging of the public

transport routes and changing over to vehicles running on gas motor fuel.

1 INTRODUCTION

Transition to “green” economy is unique for each

country, being affected by various interrelated

factors. However, the main trends and challenges

have been shaped by global processes and are

relevant for both developed and developing

countries. One of those is urbanization, which is an

objective process triggered by social demands,

modes of production, and character of the social

system. As a consequence, a precipitous growth of

urban population, especially in recent decades, has

depleted the reproductive capacity of the

environment in major cities.

As reported by the European Commission

(Eurostat, 2008), transport in 27 EU countries is a

major source of greenhouse gas (GG) emissions,

second after the industry, and their dynamics is

higher than that of any other energy-generating

sector (Transport and its infrastructure).With that,

the share of automotive transport exhaust is over

90% of direct transport exhaust (Eurostat, 2009), and

it is increasing in most countries due to growing

transportation volumes. In large cities, the

atmospheric pollution comes mainly from

automotive transport. Thus, in Moscow and other

Russian megacities the share of automobile exhaust

is over 90% of total emissions to the atmosphere.

The share of vehicle exhaust in less industrialized

cities is but a little smaller (about 80-90). On the

average in Russia, the vehicle emissions account for

42% of the total (Konstantinov, 2012).

One of Russia’s priorities on the way to

sustainable development of the socio-economic

system is transition to low-carbon fuels. More

economical and environmentally friendly vehicles

will facilitate «green» growth, diminish the

environmental loads and increase the processing

depth of natural resources. Considering the public

concern regarding the sustainability of urban

territories and increasing human migration to cities

worldwide, there has been developed a project

concept of a system of city management taking into

account both the mobility needs of population and

environmental factors.

In compliance with the “road map” given by the

European Economic Commission to intelligent

transport systems for the period of 2012-2020 (ITS

for sustainable mobility, 2012) there have been

identified 20 lines of activities promoting the use of

ITS. They incorporate both activities on developing

of uniform terminology and understanding of the

ITS essence and objectives, and measures on

introduction of ITS-related developments. This

concerns both the technical component

(development of road-to-vehicle and vehicle-to-

vehicle communication; integration of different

kinds of transport) and activities on improving of

112

Makarova, I., Khabibullin, R., Belyaev, E. and Mavrin, V.

Managing an Urban Transport System in Enhancing the Area Stability.

In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2016), pages 112-117

ISBN: 978-989-758-185-4

management and safety of the transport system,

including the environmental safety, as well as

analytical work and development of various

methodologies. Since optimizing of transport system

parameters may follow along two lines, i.e. by

regulating the transport density and by improving

the environmental safety of transport means,

pathways for achieving the best values are being

developed in two directions. It is evident that the

ITS plays an important role in optimization of the

transport system operations. It promotes the

sustainability of the environment (Fengqi, 2010),

diminishes the negative impact of the transport

complex on the environment and also the energy

consumption (Gkritza, 2013). Nowadays the ITS has

turned into tool in transport planning being used for

surveying, decreasing of traffic congestions (Harb,

2011), and planning of shared vehicle use. Since the

ITS is a technology for creating applications and

systems for traffic management and forestalling of

accidents, they diminish the workload on motorists

(Jarašūniene, 2013).

2 EXPERIMENTAL METHODS

2.1 Intelligent Transport Systems

Since the transport system belongs to the class of

major systems, optimizing of its processes involves

processing of great bodies of data and modeling of

processes by using IT technologies. This kind of

research is joined by a common term “intelligent

transport systems”. Alongside with artificial intellect

systems this area is dynamically developing and

embraces different classes of problems.

The sustainability of transport systems in major

cities, including megacities, depends on the stability

of subsystems and links connecting them, which in

turn depends on the quality of management.

Nowadays controlling of large systems, such as the

transport one, is effected by using the systems of

solution support, expert systems, and information

systems of control. Such systems are created for

both strategic and local, tactical, control. Efficient

management not only improves the economic

performance of the system; it also assists socially, by

improving the transport service and reducing the

negative impact of the transport complex on the

environment.

Methodologically, the ITS development is based

on systemic approach, meaning that ITS are created

as systems, not as individual modules (services). The

process involves forming of a unified, open

architecture of the system, protocols of information

exchange, forms of transportation documents,

standardization of parameters of communication,

control and management technologies, management

procedures, etc.

Intellectualization enhances the transport

system’s safety and efficiency both due to

information services and the means and methods

affording to perform intelligent data analyses and

make decisions on their basis. Regarding ITS as an

information service, the authors of work have found

that such systems are mainly needed for informing a

driver of the transport flow parameters along his

route. The ITS tools are also used to ensure the

safety of traffic participants, i.e. in intelligent

onboard systems (safety shields) and pedestrian

recognition systems (Truong Cong, 2011). The

development of alerting systems is connected with

developing of communication technologies and

infrastructure applied in traffic control. Several

studies have been devoted to the analysis of trends

and future evolution of ITS (Ran, 2012;).

2.2 Decision Support Systems in

Management of Transport Flows

Since the quality of decisions in managing of large

systems depends on the quality of information,

adequately selected methods of its analysis, and

effective tools, this calls for creating of decision

support systems. The structure of DSS essentially

depends on the kind of objectives to be tackled, on

the available data, information and knowledge and

also on the system’s users. Therefore a DSS consists

of three main parts:

1. A data system for collecting and storing of

information obtainable from internal and external

sources; as a rule it is a data storage.

2. A dialogue system affording the user to set

the data to be selected and methods for their

processing.

3. A system of models, i.e. ideas, algorithms

and procedures permitting to process and analyse the

data.

Since decision making is based on the real data

of the object under management, both analysis and

adopting of strategic decisions require aggregate

information available from a specially created data

storage (DS). Data storages contain the information

collected from several operative databases of an on-

line transaction processing systems (OLTP). The

core of a road situation control center is a multi-

dimensional intelligent data model (an OLAP cube)

Managing an Urban Transport System in Enhancing the Area Stability

113

which collects, stores and formalizes the road

network parameters (Fig. 1).

Figure 1: OLAP cube: the “kind of transport” dimension.

Storing of information as an OLAP cube and its

subsequent processing will make it possible to

precisely assess the dynamics of street road network

parameters in different dimensions (number of

transport vehicles, road section, season, of the year,

average speed, availability of traffic lights, etc.). By

analyzing the information on varying road

parameters within the day time, week day, and

month it is possible to forecast probable changes in

road situations in the future.

Besides, modeling of variants of possible

solutions with varying system parameters allows to

select the optimal parameters and create a database

of best solutions at fixed parameters of the transport

flow and external environment. Such bases serve for

operative decision making in the case of transport

emergencies. The intellectual core of the DSS is

often composed by simulation models, which affords

not only to make a qualitative analysis of the

processes but to investigate the consequences of

variations and select the variant satisfying all preset

limitations to obtain the system parameters optimal

for the preset conditions (Makarova, 2013).

3 RESULTS AND DISCUSSION

3.1 Field Studies of Transport Flows

Improving the performance of a transport system

presupposes examining of its current state, revealing

of problem areas, developing of a package of

measures, aimed at alleviating the negative factors,

and implementing the measures while controlling

the parameters in order to assess the solutions

efficiency. Therefore the following objectives have

been set:

 To carry out field studies of the transport

flows, including their intensity and sites with

frequently occurring accidents;

 To carry out field studies on pollutants

distribution in atmospheric air;

 To develop a software module for feeding,

storing and analyzing of the obtained statistical

data;

 Developing of a model simulating the city’s

traffic infrastructure;

 Input and systematization of obtained

information and of a database by using a

software complex;

 Analysis of information on the traffic

infrastructure parameters by using the

statistical analysis of obtained information;

 Model studies of the impact of vehicles

quality.

The field studies were conducted using the

following methods of monitoring the traffic

infrastructure: revealing the regularities of transport

flows formation; evaluating the most congested road

sections and sites of frequent accidents. First, the

city map was split into squares to reveal the

potentially problematic sites (narrowing sections of

thoroughfares and streets, traffic confluence,

complecated road junction with lots "conflicts

points", etc.), with account for peculiarities of the

city’s street-road network (SRN) pattern.

When identifying the sites with higher than

average traffic intensity, we analysed the transport

police data and revealed the places with a high rate

of road traffic accidents. It was hypothesized that the

high accident rates were caused by increased traffic

loads on the SRN. The experiment included

registering by vehicle dash cameras and video

cameras and computer processing of the images.

Measurements were performed at the cross roads

that had been found as the greatest loaded,

potentially dangerous and most in need of

optimization. In order to reveal the peak loads on

SRN, the measurements were performed during the

week days and included the following periods: 1)

07:00–09:00 am; 2) 11:30–13:30 pm; 3) 16:30–

18:30 pm. The following parameters were recorded:

vehicle model, brand and type; traffic direction;

average current velocity.

3.2 Studies of the Atmospheric Air

Pollution

Having analyzed the road layout, the purpose of motor

roads, and information about the traffic load, we

selected the following 5 road portions for instrumental

measurement of ambient air pollution on the following

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roadside clear zones. Sampling of atmospheric air was

carried out in the immediate vicinity of roads, mainly

from downwind, during the morning and evening rush

hours. For each site there were obtained 2 duplicate

samples, so that the total number of samples was 600.

Both the initial working solutions (600) and 300 blank

solutions were prepared for analysis at a

chromatograph with a photoionization detector and

photometer. Analysis for the presence of carbon

monoxide has required the obtaining and processing of

more than 300 chromatograms.

Company's fixed contamination sources of

Naberezhnye Chelny city are outside the city,

therefore motor transport is the main source of

emissions. We made the environmental analysis,

determined locations of high concentrations of

pollutants and calculated emissions of motor

transport by using certified methodologies: in this

calculation traffic volume was considered. The

volume was determined by making field surveys.

Maps of pollutants (CO, NO

, C

, soot, SO

formaldehyde, benzopyrene) were made by using

Integral Ecolog 3.0 software. The analysis of these

maps showed that the highest levels of concentration

of polluting substances are located on the

crossroads. Field surveys showed, that maximal

density of traffic cause increasing of concentration

of polluting substance. We have ascertain, that non-

optimum traffic light control is one of the cause of

this situation. Further research is aimed at searching

for optimal parameters of traffic light control by

using simulation models and at the elaborations of

recommendations for optimization of road traffic.

3.3 Developing of Decision Support

System

Using the maps of pollutant dispersion from

stationary sources, derived as a result of field

observations (Figure 2), we highlighted the problem

areas, one of which is a complex junction formed by

intersecting Mira, Druzhba Narodov and

Syuyumbike Avenues.

Figure 2: Dispersion of carbon monoxide map.

As shown statistically, this site is characterized

by a higher than average rate of vehicle accidents,

which cumbers the traffic and deteriorates the

atmosphere by frequently occurring congestions. For

more detailed analysis of the area, we constructed a

simulation model using the AnyLogic software

(Russia). The following factors were taken into

account: geometry of the road network portion;

traffic density; intensity of pedestrian traffic along

traffic lanes; pollutants actually emitted by motor

vehicles emission quotas; modes of traffic lights

operation at road portions preceding and following

the portion being analyzed. A constraint for the

model was the value of the emission quota that was

not to be exceeded. This system of simulation

modeling makes possible to examine the effect of

varying transport flow parameters on the city

atmosphere and select the environmentally optimal

parameter values.

The input data were systematized in tables and

then fed into a database using the developed input

forms. Account was made for the transport flow

parameters (its composition and intensity) and the

data on the emission of noxious substances into the

atmosphere. The site for the field study represented a

confluence of two major avenues and

accommodated several public transport routes

connecting the city’s newer and older parts.

Therefore optimizing of the traffic current

parameters primarily involved the route network.

Another approach consisted in using of buses with

greater capacity, which would both diminish the

traffic current density and reduce emissions of

harmful substances. Similar methods are described

in works (Saharidis, 2013). The optimization

experiment on a simulation model was carried out

using an OptQuest device and metaheuristic

methods.

At the first stage of the experiment, we

determined the optimal parameters for the traffic

current, such as density, intensity and speed, falling

within the quota for pollutant emissions.

Table 1: Point sizes and type styles.

Substance

name

The volume of emissions

100% fleet

on diesel

fuel

50% fleet

on gas

motor fuel

100% fleet on

gas motor fuel

CO 1.036 0.870 0.691

NOx 0.974 0.907 0.830

CH 0.499 0.437 0.386

Soot 0.581 0.102 0.043

SO2 0.578 0.422 0.361

formaldehyde 0.681 0.663 0.627

Benzapyrene 0.579 0.514 0.489

Managing an Urban Transport System in Enhancing the Area Stability

115

At the second stage, volumes of vehicular

emissions were determined. While preserving the

original parameters, we replaced a part of the public

transport with more environmentally friendly

vehicles. This considerably reduced the volumes of

emitted pollutants (Table 1).

The city is crossed by a longitudinal

thoroughfare comprising the Musa Djalil,

Naberezhnochelninsky, and Mira Avenues. The

traffic intensity, the highest among the city streets, is

3000 veh/h at a capacity of 3500 veh/h. During the

rush hours the vehicle flow gets stuck, the traffic

gets congested, with frequently occurring

bottlenecks. Maneuvering within the vehicle current

is hindered at turnarounds. There is an dangerous

area adjoining the “Pedinstitut” bus stop, where the

roadway narrows from three to two lanes, after

crossing with Nizametdinov street, as a result of

which vehicles are forced to change lines

immediately after the crossroads. At the nearest road

sections, inadequate traffic management has resulted

in even more dangerous situations. Thus, of the city

total of 472 vehicle crashes during 8 months of

2015, 37 in this avenue.

For more detailed analysis, we designed a

simulation model of the road section that took into

account the following parameters: the section

geometry, traffic flow density, intensity of

pedestrian flow, signalization modes at preceding

and succeeding sections, and the phase number in

traffic signals. As revealed by simulation

experiments, the traffic is negatively affected by

such factors as great density, inappropriate section

geometry, and numerous infrastructural elements

(such as public transport stops and turnarounds). It

was also proved that the flow density at

Naberezhnochelninsky Avenue is affected by two

unregulated turnarounds.

To reduce the aforementioned impacts, we

proposed a scheme of optimization of the crossing

layout, which included elimination of the nearest U-

turn with the lowest discharge capacity and using of

an alternative number of phases in the crossing

regulation, that is the combination of main and

intermediate regulation cycles. Correspondingly, for

changing of phase number we proposed to change

the time intervals between the signals.

Using the enumerative technique based on

metaheuristics, we determined the most appropriate

infrastructure for traffic signalization. A

conventional traffic light with two operation modes

and each phase of 148 seconds was substituted for a

multiphase timing signal automatically correcting

the phases and time of coordination of a set of

signalizing objects adapting to the traffic situation.

To set the adaptive mode in a traffic light, the

model processed the situations at several regulated

turnarounds located in the immediate proximity of

the road section and inter-influenced by the traffic

flow. The model registered the speed and density of

the traffic flow at each section. By analyzing these

characteristics, coming in real-time regime as

feedback from succeeding road system sections, and

it is possible to correct the length of green signal for

all directions according to optimal regimes

established in the model. Table 2 presents changing

of signal phases with adaptive control depending on

the traffic flow density. Adaptive regulation will

permit to smoothly relieve all sections of the road-

street system by responding to critical flow

parameters at individual sections and so forestalling

congestions and bottlenecks at succeeding sections.

Table 2: Variation of signal phase depending on flow

density.

Flow density at

the section

Total phase time,

sec.

Red signal (main

cycle), sec.

Green signal

(main cycle), sec.

Red and yellow

signal, sec.

Yellow signal,

sec.

Green with yellow

signal, sec.

95% 112 40 64 2 4 2

82% 112 42 62 2 4 2

74% 86 43 35 2 4 2

61% 86 48 30 2 4 2

Another problem area in the city of Naberezhnye

Chelny is located at the intersection of Chulman and

Druzhba Narodov Avenues (Figure 3a). According

to the traffic police data, it is a place with a high

concentration of road crashes, which both prevents

the normal operation of the transport system and

affects the environment. For more detailed analysis,

we designed a simulation model taking into account

the road section geometry, the intensity of pedestrian

flow, and traffic light modes at preceding and

succeeding sections. The target function was the

average speed of vehicles since the frequent road

crashes here are caused by high speed. The

simulation results have revealed that the traffic

quality at this section is affected by the following

factors: high flow density, high average speed of

vehicles, and the presence of an unregulated

pedestrian crosswalk.

The results of the road section survey can be

seen in Table 3. The data indicate to the possibility

of radical improvement of the traffic parameters.

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116

Figure 3: Simulation model: а) before optimizations; б)

after optimizations.

Table 3: Calculated parameters of the road section of

interest.

Parameter name Value before

modification

Value after

modification

Traffic flow parameters

Average speed, km/h at the

section

12 27

Number of stops per unit of

time, pcs

4 1

Flow density relatively the

road, %

92 67

Average time needed to

traverse the section, min

4 1,4

Air pollutants concentration

CO 1.042 0.879

NOx 0.972 0.937

CH 0.495 0.458

Soot 0.583 0.122

0.574 0.428

To reduce the negative impacts, we proposed a new

intersection layout, involving a signal control. After the

model was modified (Figure 3b), experiments showed

that using the proposed variant will diminish the

likelihood of road crashes and stabilize the parameters

of vehicular and pedestrian flows.

The proposed plan of reconstruction of crossroad

allowed both to decrease accident hazard of the site

significantly at a lower cost and to decrease negative

impact on the environment. Also optimal traffic light

mode was determined which allowed to increase

pedestrian safety. Traffic police approved the

proposed solution and the reconstruction of the

crossroad had been carried out.

4 CONCLUSIONS

The research findings have demonstrated a

considerable contribution of automotive

transportation to urban air pollution. It has been

established that the problem should be approached

comprehensively. Simulation modelling can help to

identify the optimal parameters for the transport

flow and find rational managerial solutions.

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