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Innovative Technology for Evaluation of Capacity of Thoroughfares
Available online at www.sciencedirect.com
ScienceDirect Transportation Research Procedia 20 (2017) 671 – 675
12th International Conference "Organization and Traffic Safety Management in large cities", SPbOTSIC-2016, 28-30 September 2016, St. Petersburg, Russia
Innovative Technology for Evaluation of Capacity of Thoroughfares Evgeniy Vitvitskii a, Mari Simul b, Svetlana Porkhacheva c* Siberian State Automobile and Highway Academy, 5 Mira av., Omsk, 644080, Russia
Abstract The article sets forth basic provisions for evaluation of the level of service of thoroughfares required to solve the problems with traffic management related to the planning and control of transport and pedestrian flows, assessment of traffic condition, reconstruction of the street and road network. The level of service is hard to estimate due to numerous factors which affect traffic intensity and capacity. The article provides multiplicative power law models to determine the travel speed of vehicles on a road section and intervals between vehicles at an intersection which allow to evaluate the capacity of any section of the city street and road network with regard to the existing and predicted set of factors. © 2017 2016The TheAuthors. Authors. Published Elsevier © Published by by Elsevier B.V.B.V. 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 the 12th International Conference "Organization and Traffic Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety in large cities". Safety Management Management in large cities” Keywords: Street and road network, level of service, capacity, arterial road.
1. Introduction The analysis of the impact of different factors on the capacity can be found in numerous works [Krasnikov (1998), Lobanov et al. (1970), Silyanov (1977), Resende and Paulo (1994)]. The factors listed in these works have a different level of impact on the capacity which makes them more or less dominating. The work [Khrapova (2006)] studies the impact of concentration of traffic along the main arterial roads, shopping and business centers, which decreases significantly the capacity of arterial roads due to traffic hindrances like street parking, numerous pedestrian crossings and public transport stops. Therefore, an applicable (feasible) procedure for evaluating the decrease in capacity is
* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected] a, [email protected] b, svetlanam82mail.ru c*
2352-1465 © 2017 The Authors. Published by Elsevier B.V. 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 the 12th International Conference “Organization and Traffic Safety Management in large cities” doi:10.1016/j.trpro.2017.01.109
672
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
required to handle the issues with assessment and efficiency of traffic management in a street and road network as well as ensure normal functioning of public transport. 2. Main text Any arterial road indeed has a great number of traffic hindrances which constitute actual traffic conditions and are caused by the factors which reduce connectivity and, consequently, capacity of the arterial road (route) or a section of the street and road network. The nature of traffic hindrances having influence on city routes has been covered in sufficient detail while their level of impact on the capacity of the street and road network has been neglected due to the variety of their combinations and a great many factors which intensify or reduce the resulting impact of these hindrances on the travel speed. A hypothesis can be assumed here regarding the impact of a combination of common factors on the travel speed and traffic conditions. We have undertaken several experimental researches to reveal the impact level of specific factors on the travel speed which resulted in a general list of traffic hindrances shown in Table 1 [Khrapova (2006)]. Table 1. Traffic hindrances in city environment No. 1
2 3
Name of traffic hindrance
Frequency, %
Transport intersection: controlled/uncontrolled
23/7
Pedestrian crossing: Controlled/uncontrolled Unorganized pedestrian traffic through the road way
13/5 5
Public transport stops: 4
- municipal public transport stop
9
- unorganized
4
Vehicle maneuvering: 5
- lane change
2
- emerging of vehicles from adjacent territory
3
Street parking: 6
- marked with angled parking spaces
1
- on the roadside
3
- unmarked on the roadside
14
7
Traffic sign 3.24 "Speed limit"
7
8
Speed bump
4
The research results showed that major speed drops occur at signal-controlled intersections as well as in road sections with street parking spaces, marked pedestrian crossings, public transport stops [Simul (2009)]. This explains the need for prompt monitoring and evaluation of traffic conditions with regard to the combination of influencing factors. The main parameter which defines the properties of a transport network is the capacity of its thoroughfares. The value derived from capacity is the level of service factor Кz, which determines the relation of the actual intensity of traffic flows to the capacity of a specific thoroughfare [Khrapova and Ryabkova (2009)]. The calculation of capacity using simplified dynamic models according to the value of safe distance between vehicles going one after another is based on the travel speed and dynamic gauge [Elvik et al. (2001)]. Therefore, the approach adopted in the work develops a multiple factor model for determining the travel speed on a road section with regard to typical influencing factors: presence or absence of street parking spaces, public transport stops and marked pedestrian crossings.
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Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
This work uses an empirical function as a multiple factor model for evaluating the travel speed. The function can be derived from univariate dependences of the travel speed on the parameters of traffic hindrances. Each of the dependencies shall be represented by a relevant value varying within some limits. In general terms the travel speed V along a road section can be calculated by [Khrapova and Ryabkova (2009)]: а) with a parking space, public transport stop, pedestrian crossing in a road section:
V
F(l)q G(h)b D(n)m P(s)u B(w)r K(x) C(y),
(1)
2) with a public transport stop, pedestrian crossing, but with no parking space:
V
D(n)m P(s)u B(w)r K(x) C(y),
(2)
where F(l) – travel speed vs. parking length l, m; G(h) – travel speed vs. parking width h, m; D(n) – travel speed vs. traffic intensity n, vehicles/h; P(s) – travel speed vs. composition of traffic flow s,%; B(w) – travel speed vs. traffic lane width w, m; K(x) – travel speed vs. distance between adjacent uncontrolled pedestrian crossings, m; C(y) – travel speed vs. distance between adjacent public transport stops, m; q,b,m,u,r – degrees of a polynomial. Numeric values of degrees q, b, m, u depend on the results of experiments. The members of equations К(х) and С(у) define the impact of the relative position of the public transport stops and pedestrian crossings on variations of speed V. Application of equation (2) is given in Table 2. This application covers most typical traffic conditions in a city street and road network. Table 2. Application of the model for evaluating the travel speed in a road section Parameter
Symbol
Parameter variation range
Length of parking
l
From 10 to 170 m
Width of parking
h
From 0 to 3.5 m
Traffic intensity
n
From 80 vehicles/h to 1350 vehicles/h
Composition of traffic flow
s
From 40 to 100% of cars in a traffic flow
Width of lane
w
From 2.25 m to 4.55 m
Distance between pedestrian crossings
x
From 200 m to ∞
Distance between public transport stops
y
From 400 m to ∞
We have applied some equations to show the relation between speed variations and traffic factors to evaluate the dependence of speed variations of vehicles on the parameters of pedestrian traffic. The proposed model can be described by the following equations [Simul (2010)]:
䌖V(U j ) 䌖U j
5
V(u0 )
䌥䌖䌖VU(u) u 1
Uj ,
(3)
j
where V(Uj) – speed exposed to the influence by the factors u1 – u5; j – factor number, j= 1,...5 ; u1 – pedestrian flow intensity; u2 – width of crossing; u3 – width of road way; u4 – distance between crossings located along the arterial road one after another; u5 – distance between stops located one after another. Below are given empirical formulas which explain speed variations through traffic factors defined by the relevant univariate dependence with regard to the position of the lane within a road way, for arterial road traffic between intersections:
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Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
F(l) аl2 вl с ; аh2 вh с ;
G(h)
D(n) аn2 вn с B(w)
(4)
аw вw с ; 2
P(s) аs2 вs c , where l – length of parking, m; h – width of parking, m; n – traffic intensity, vehicles/h; w – width of lane, m; s – composition of traffic flow, %; а, в, с – free members of equations. We should mention here that the experimental researches (with over 5000 measurements performed) showed that making the parking wider will reduce the speed of vehicles depending on the degree of narrowing of the right lane as well as on the parking length which makes a driver change lanes. A major speed drop in the right most lanes will occur when the road way is made narrower for more than 1 m and when the parking space is made longer. Figures 1 and 2 give speed variations vs. length of parking.
Fig. 1 Speed vs. length of parking
Speed drop down to 50% from the speed limit occurs for the lane traffic intensity of 800 – 850 vehicles/h. Making the traffic lane wider and increasing the number of cars will make the travel speed higher. Dependencies (1,2) are design models for the impact of multiple factors on average speed in a traffic flow in a specific arterial road section with a specific combination of traffic conditions and, when the speed is known, allow to calculate the capacity using the following equation:
Рп
Т эф Vф
,
(5)
Lg where Рn – capacity, vehicles/h; Тэф – time allocated for letting a vehicle pass in a given direction, sec; Vф – actual speed, m/sec; Lg – average dynamic gauge, m/vehicle. The proposed procedure for evaluating the level of service of city arterial road sections was applied for developing the "Target Program for Mitigating the Risks of Accidents in a Street and Road Network of the Central Administrative
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
District of the city of Omsk", as well as in scope of works under a municipal contract with the Department of Transport of Omsk. 3. Conclusion The developed models for evaluating the speed of traffic flows in a street and road network provide the following: x determine an average traffic speed in street and road network sections which are exposed to the impact of different combinations of traffic factors; x determine the capacity, level of service for both local sections of arterial roads and the whole transport network (as well as arterial road or a city route), by using a parameter set which affects the travel speed of vehicles; x find the sections of an arterial road without any capacity margin; x plan the distribution of traffic flows through the street and road network with regard to the actual traffic conditions based on predicted road capacity; x make decisions on arrangement of street parking spaces in roadside area; x develop the actions to mitigate traffic hindrances affecting the capacity of specific areas of arterial roads; x prepare recommendations for arrangement and development of street parking spaces with regard to the level of service of adjacent sections of the arterial road. References: Elvik, R., Burger, M. A., Vaa, T. (2001). Guide on traffic safety: trans. from Norwegian. Moscow: MADI, 754 p. Khrapova, S.M. (2006). Management of traffic affecting factors in road junctions. In proceedings of I Russian scientific conference "Design, construction and operation issues of transport facilities", pp. 153–159. Khrapova, S.M., Ryabokon, Y.A. (2009). On evaluation of the level of service of city transport network thoroughfares. Motor transport enterprise, (3): 49–50. Khrapova, S.M., Ryabokon, Y.A. (2009). On research results of impact of road factors on variations of traffic conditions. Bulletin of IRTU, 1(37): 70–74. Krasnikov, A. N. (1998). One lane road traffic behavior. Moscow: Transport, 111 p. Lobanov, E.M., Silyanov, V.V., Sitnikov, Y.M., Sapegin, L.N. (1970). Road capacity. Moscow: Transport, 152 p. Resende, Paulo Tarso Vilela. (1994). Development and validation of volume-to-capacity based accident prediction models. USA: University of Illinois at Urbana–Champaign, Illinois: 394 p. Silyanov, V.V. (1977). Theory of transport flows in road design engineering and traffic management. Moscow: Transport, 303 p. Simul, M.G. (2009). Impact of some factors on traffic in city arterial roads. In proceedings of 63 Scientific Conference, Omsk, pp. 20–23. Simul, M.G. (2010). Experimental research of transport flow characteristics with regard to pedestrian flow in city arterial roads. In proceedings of 64 scientific conference devoted to the 80-year anniversary of the academy, Omsk, pp. 52–55.
675
ScienceDirect Transportation Research Procedia 20 (2017) 671 – 675
12th International Conference "Organization and Traffic Safety Management in large cities", SPbOTSIC-2016, 28-30 September 2016, St. Petersburg, Russia
Innovative Technology for Evaluation of Capacity of Thoroughfares Evgeniy Vitvitskii a, Mari Simul b, Svetlana Porkhacheva c* Siberian State Automobile and Highway Academy, 5 Mira av., Omsk, 644080, Russia
Abstract The article sets forth basic provisions for evaluation of the level of service of thoroughfares required to solve the problems with traffic management related to the planning and control of transport and pedestrian flows, assessment of traffic condition, reconstruction of the street and road network. The level of service is hard to estimate due to numerous factors which affect traffic intensity and capacity. The article provides multiplicative power law models to determine the travel speed of vehicles on a road section and intervals between vehicles at an intersection which allow to evaluate the capacity of any section of the city street and road network with regard to the existing and predicted set of factors. © 2017 2016The TheAuthors. Authors. Published Elsevier © Published by by Elsevier B.V.B.V. 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 the 12th International Conference "Organization and Traffic Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety in large cities". Safety Management Management in large cities” Keywords: Street and road network, level of service, capacity, arterial road.
1. Introduction The analysis of the impact of different factors on the capacity can be found in numerous works [Krasnikov (1998), Lobanov et al. (1970), Silyanov (1977), Resende and Paulo (1994)]. The factors listed in these works have a different level of impact on the capacity which makes them more or less dominating. The work [Khrapova (2006)] studies the impact of concentration of traffic along the main arterial roads, shopping and business centers, which decreases significantly the capacity of arterial roads due to traffic hindrances like street parking, numerous pedestrian crossings and public transport stops. Therefore, an applicable (feasible) procedure for evaluating the decrease in capacity is
* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected] a, [email protected] b, svetlanam82mail.ru c*
2352-1465 © 2017 The Authors. Published by Elsevier B.V. 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 the 12th International Conference “Organization and Traffic Safety Management in large cities” doi:10.1016/j.trpro.2017.01.109
672
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
required to handle the issues with assessment and efficiency of traffic management in a street and road network as well as ensure normal functioning of public transport. 2. Main text Any arterial road indeed has a great number of traffic hindrances which constitute actual traffic conditions and are caused by the factors which reduce connectivity and, consequently, capacity of the arterial road (route) or a section of the street and road network. The nature of traffic hindrances having influence on city routes has been covered in sufficient detail while their level of impact on the capacity of the street and road network has been neglected due to the variety of their combinations and a great many factors which intensify or reduce the resulting impact of these hindrances on the travel speed. A hypothesis can be assumed here regarding the impact of a combination of common factors on the travel speed and traffic conditions. We have undertaken several experimental researches to reveal the impact level of specific factors on the travel speed which resulted in a general list of traffic hindrances shown in Table 1 [Khrapova (2006)]. Table 1. Traffic hindrances in city environment No. 1
2 3
Name of traffic hindrance
Frequency, %
Transport intersection: controlled/uncontrolled
23/7
Pedestrian crossing: Controlled/uncontrolled Unorganized pedestrian traffic through the road way
13/5 5
Public transport stops: 4
- municipal public transport stop
9
- unorganized
4
Vehicle maneuvering: 5
- lane change
2
- emerging of vehicles from adjacent territory
3
Street parking: 6
- marked with angled parking spaces
1
- on the roadside
3
- unmarked on the roadside
14
7
Traffic sign 3.24 "Speed limit"
7
8
Speed bump
4
The research results showed that major speed drops occur at signal-controlled intersections as well as in road sections with street parking spaces, marked pedestrian crossings, public transport stops [Simul (2009)]. This explains the need for prompt monitoring and evaluation of traffic conditions with regard to the combination of influencing factors. The main parameter which defines the properties of a transport network is the capacity of its thoroughfares. The value derived from capacity is the level of service factor Кz, which determines the relation of the actual intensity of traffic flows to the capacity of a specific thoroughfare [Khrapova and Ryabkova (2009)]. The calculation of capacity using simplified dynamic models according to the value of safe distance between vehicles going one after another is based on the travel speed and dynamic gauge [Elvik et al. (2001)]. Therefore, the approach adopted in the work develops a multiple factor model for determining the travel speed on a road section with regard to typical influencing factors: presence or absence of street parking spaces, public transport stops and marked pedestrian crossings.
673
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
This work uses an empirical function as a multiple factor model for evaluating the travel speed. The function can be derived from univariate dependences of the travel speed on the parameters of traffic hindrances. Each of the dependencies shall be represented by a relevant value varying within some limits. In general terms the travel speed V along a road section can be calculated by [Khrapova and Ryabkova (2009)]: а) with a parking space, public transport stop, pedestrian crossing in a road section:
V
F(l)q G(h)b D(n)m P(s)u B(w)r K(x) C(y),
(1)
2) with a public transport stop, pedestrian crossing, but with no parking space:
V
D(n)m P(s)u B(w)r K(x) C(y),
(2)
where F(l) – travel speed vs. parking length l, m; G(h) – travel speed vs. parking width h, m; D(n) – travel speed vs. traffic intensity n, vehicles/h; P(s) – travel speed vs. composition of traffic flow s,%; B(w) – travel speed vs. traffic lane width w, m; K(x) – travel speed vs. distance between adjacent uncontrolled pedestrian crossings, m; C(y) – travel speed vs. distance between adjacent public transport stops, m; q,b,m,u,r – degrees of a polynomial. Numeric values of degrees q, b, m, u depend on the results of experiments. The members of equations К(х) and С(у) define the impact of the relative position of the public transport stops and pedestrian crossings on variations of speed V. Application of equation (2) is given in Table 2. This application covers most typical traffic conditions in a city street and road network. Table 2. Application of the model for evaluating the travel speed in a road section Parameter
Symbol
Parameter variation range
Length of parking
l
From 10 to 170 m
Width of parking
h
From 0 to 3.5 m
Traffic intensity
n
From 80 vehicles/h to 1350 vehicles/h
Composition of traffic flow
s
From 40 to 100% of cars in a traffic flow
Width of lane
w
From 2.25 m to 4.55 m
Distance between pedestrian crossings
x
From 200 m to ∞
Distance between public transport stops
y
From 400 m to ∞
We have applied some equations to show the relation between speed variations and traffic factors to evaluate the dependence of speed variations of vehicles on the parameters of pedestrian traffic. The proposed model can be described by the following equations [Simul (2010)]:
䌖V(U j ) 䌖U j
5
V(u0 )
䌥䌖䌖VU(u) u 1
Uj ,
(3)
j
where V(Uj) – speed exposed to the influence by the factors u1 – u5; j – factor number, j= 1,...5 ; u1 – pedestrian flow intensity; u2 – width of crossing; u3 – width of road way; u4 – distance between crossings located along the arterial road one after another; u5 – distance between stops located one after another. Below are given empirical formulas which explain speed variations through traffic factors defined by the relevant univariate dependence with regard to the position of the lane within a road way, for arterial road traffic between intersections:
674
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
F(l) аl2 вl с ; аh2 вh с ;
G(h)
D(n) аn2 вn с B(w)
(4)
аw вw с ; 2
P(s) аs2 вs c , where l – length of parking, m; h – width of parking, m; n – traffic intensity, vehicles/h; w – width of lane, m; s – composition of traffic flow, %; а, в, с – free members of equations. We should mention here that the experimental researches (with over 5000 measurements performed) showed that making the parking wider will reduce the speed of vehicles depending on the degree of narrowing of the right lane as well as on the parking length which makes a driver change lanes. A major speed drop in the right most lanes will occur when the road way is made narrower for more than 1 m and when the parking space is made longer. Figures 1 and 2 give speed variations vs. length of parking.
Fig. 1 Speed vs. length of parking
Speed drop down to 50% from the speed limit occurs for the lane traffic intensity of 800 – 850 vehicles/h. Making the traffic lane wider and increasing the number of cars will make the travel speed higher. Dependencies (1,2) are design models for the impact of multiple factors on average speed in a traffic flow in a specific arterial road section with a specific combination of traffic conditions and, when the speed is known, allow to calculate the capacity using the following equation:
Рп
Т эф Vф
,
(5)
Lg where Рn – capacity, vehicles/h; Тэф – time allocated for letting a vehicle pass in a given direction, sec; Vф – actual speed, m/sec; Lg – average dynamic gauge, m/vehicle. The proposed procedure for evaluating the level of service of city arterial road sections was applied for developing the "Target Program for Mitigating the Risks of Accidents in a Street and Road Network of the Central Administrative
Evgeniy Vitvitskii et al. / Transportation Research Procedia 20 (2017) 671 – 675
District of the city of Omsk", as well as in scope of works under a municipal contract with the Department of Transport of Omsk. 3. Conclusion The developed models for evaluating the speed of traffic flows in a street and road network provide the following: x determine an average traffic speed in street and road network sections which are exposed to the impact of different combinations of traffic factors; x determine the capacity, level of service for both local sections of arterial roads and the whole transport network (as well as arterial road or a city route), by using a parameter set which affects the travel speed of vehicles; x find the sections of an arterial road without any capacity margin; x plan the distribution of traffic flows through the street and road network with regard to the actual traffic conditions based on predicted road capacity; x make decisions on arrangement of street parking spaces in roadside area; x develop the actions to mitigate traffic hindrances affecting the capacity of specific areas of arterial roads; x prepare recommendations for arrangement and development of street parking spaces with regard to the level of service of adjacent sections of the arterial road. References: Elvik, R., Burger, M. A., Vaa, T. (2001). Guide on traffic safety: trans. from Norwegian. Moscow: MADI, 754 p. Khrapova, S.M. (2006). Management of traffic affecting factors in road junctions. In proceedings of I Russian scientific conference "Design, construction and operation issues of transport facilities", pp. 153–159. Khrapova, S.M., Ryabokon, Y.A. (2009). On evaluation of the level of service of city transport network thoroughfares. Motor transport enterprise, (3): 49–50. Khrapova, S.M., Ryabokon, Y.A. (2009). On research results of impact of road factors on variations of traffic conditions. Bulletin of IRTU, 1(37): 70–74. Krasnikov, A. N. (1998). One lane road traffic behavior. Moscow: Transport, 111 p. Lobanov, E.M., Silyanov, V.V., Sitnikov, Y.M., Sapegin, L.N. (1970). Road capacity. Moscow: Transport, 152 p. Resende, Paulo Tarso Vilela. (1994). Development and validation of volume-to-capacity based accident prediction models. USA: University of Illinois at Urbana–Champaign, Illinois: 394 p. Silyanov, V.V. (1977). Theory of transport flows in road design engineering and traffic management. Moscow: Transport, 303 p. Simul, M.G. (2009). Impact of some factors on traffic in city arterial roads. In proceedings of 63 Scientific Conference, Omsk, pp. 20–23. Simul, M.G. (2010). Experimental research of transport flow characteristics with regard to pedestrian flow in city arterial roads. In proceedings of 64 scientific conference devoted to the 80-year anniversary of the academy, Omsk, pp. 52–55.
675