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Traffic and environmental impacts of traffic incidents on Thessaloniki’s inner ring road
Available Available online online at at www.sciencedirect.com www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com Transportation Transportation Research Research Procedia Procedia 00 00 (2016) (2016) 000–000 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 24C (2017) 288–295 www.elsevier.com/locate/procedia
3rd Conference on Sustainable Urban Mobility, 3rd CSUM 2016, 26 – 27 May 2016, Volos, Greece
Traffic and environmental impacts of traffic incidents on Thessaloniki’s inner ring road a*, Socrates Basbasbb, Evangelos Mintsiscc, George Mintsisbb, Eleni Kariotia* Christos Taxiltarisbb aa Faculty
54124, Thessaloniki, Thessaloniki, Greece Greece Faculty of of Civil Civil Engineering, Engineering, Aristotle Aristotle University University of of Thessaloniki, Thessaloniki, 54124, of of Rural Rural and and Surveying Surveying Engineering, Engineering, Aristotle Aristotle University University of of Thessaloniki, Thessaloniki, 54124, 54124, Thessaloniki, Thessaloniki, Greece Greece cc Center for Research and Technology Hellas - Hellenic Institute of Transport, Thermi, 57001,Thessaloniki, Greece Center for Research and Technology Hellas - Hellenic Institute of Transport, Thermi, 57001,Thessaloniki, Greece bb Faculty Faculty
Abstract Abstract This This paper paper examines examines the the traffic traffic and and the the associated associated environmental environmental impacts impacts of of traffic traffic incidents incidents with with the the use use of of the the traffic traffic microscopic microscopic simulation software the option option of of simulation software Aimsun. Aimsun. The The specific specific software software simulates simulates the the movement movement of of the the individual individual vehicles vehicles and and provides provides the simulating incidents incidents at at specific specific periods periods of of time, time, thus thus constituting constituting an an appropriate appropriate simulation simulation testbed. testbed. The The study study area area includes includes aa 14km 14km simulating section of of the the Thessaloniki’s Thessaloniki’s inner inner ring ring road. road. The The traffic traffic volumes volumes used used in in the the model model are are of of aa typical typical weekday weekday and and they they are are the the outputs outputs section of aa macroscopic macroscopic traffic traffic assignment assignment model model properly properly applied applied in in the the greater greater city city area. area. Traffic Traffic counts counts made made with with the the use use of of sens sens ors of ors in in the city city road road network network were were input input in in the the traffic traffic assignment assignment model. model. Different Different scenarios scenarios of of possible possible traffic traffic incidents incidents on on the the inner inner ring ring the road in in terms terms of of location location and and duration duration were were developed developed and and evaluated evaluated using using Aimsun. Aimsun. The The impacts impacts of of defined defined incidents incidents to to the the road performance of of the the ring ring road road were were examined examined with with emphasis emphasis to to the the near near and and outer outer area area of of incident incident position. position. For For the the evaluation evaluation performance process two two different different time time periods periods (25 (25 and and 50 50 mins) mins) and and three three different different demand demand levels levels (low, (low, base-case, base-case, and and high) high) were were examined. examined. process Thus, 12 12 different different scenarios scenarios were were defined defined and and tested tested with with the the traffic traffic microscopic microscopic simulation simulation software. software. The The indicators indicators used used in in the the Thus, evaluation process process include, include, among among others, others, traffic traffic density, density, mean mean network network speed, speed, vehicle vehicle –– kilometers evaluation kilometers travelled, travelled, fuel fuel consumption, consumption, CO CO22 emissions emissions etc. etc. Results Results have have shown shown that that traffic traffic speed speed and and delays delays are are seriously seriously affected affected when when incidents incidents occur occur near near merging merging or or diverging diverging areas areas in in the the inner inner ring ring road. road. At At the the same same time time incident incident duration duration and and traffic traffic demand demand deteriorated deteriorated as as traffic traffic conditi conditi ons ons increased in in values. values. increased © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The under Authors. Published by Elsevier B.V. committee of the 3rd CSUM 2016. Peer-review responsibility of the organizing Peer-review under responsibility of the organizing Peer-review under responsibility of the organizing committee committee of of the the 3rd 3rd CSUM CSUM 2016. 2016. Keywords: traffic simulation, simulation, miscroscopic miscroscopic simulation simulation model, model, incidents incidents management, management, Thessaloniki’s Thessaloniki’s ring ring road, road, AIMSUN AIMSUN Keywords: traffic * E-mail address: address: [email protected] [email protected] * Corresponding Corresponding author. author. Tel.: Tel.: +30-698-271-1907. +30-698-271-1907. E-mail
1. Introduction A traffic incident is defined as any non-recurring event that causes a reduction of roadway capacity or an unusual increase in demand. They resulted from crashes, vehicles broke down and spilled cargo (US. Department of Transportation, 2010). Incidents are responsible for a variety of impacts both on traffic conditions and the environment.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 3rd CSUM 2016. 10.1016/j.trpro.2017.05.120
Eleni Karioti et al. / Transportation Research Procedia 24C (2017) 288–295
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Traffic incidents have long been recognized as the main contributor of congestion in road networks. Incidents cause approximately 25% of traffic congestion, and even a higher proportion for urban expressways (Giuliano G, 1989).Furthermore, extended research has showed that there is a strong relationship between the traffic congestion in an incident situation and the vehicle fuel consumption combined with the pollutants emitted (CO, CO2, NOX, HC, and Pb) (Kyoungho A., 1998, Levinson D. and Huo H. 2002). Additionally, the vehicles delays on the road network caused by incidents, cost in terms of money and time. Secondary traffic incidents at a road incidents area are observed in many cases due to increased traffic hazard created by the prevailing traffic congestion. Considering the above impacts it is clearly perceived that traffic incidents generate a significant socio-economic and environment cost. The research work described in this paper attempts to evaluate impacts caused to traffic and the environment by road incidents occurring at different locations and with different duration and demand conditions on the inner ring road of Thessaloniki. The study area includes a 14km section of the ring road and the tool used in order to perform the evaluation was the traffic microscopic simulation software AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks). 2. Microscopic traffic simulation Microscopic simulation models identify the individual movements of each vehicle and are based on their description by a vector which consists of coordinates such as vehicle’s size, speed, maximum acceleration and its position. The advantage of this type of simulation is the detailing description of traffic conditions because of the wide range of vehicles movement characteristics and road conditions that can be simulated (Charoniti E., 2013). Analyzing the microscopic traffic simulation closely, most of the models use various algorithms and models of driving behavior to simulate the movement of individual vehicle on the network. The three basic algorithm are car – following, lane – changing and gap – acceptance. Within Aimsun the interactions between vehicles are examined using a car-following and a lane – changing algorithm. Drivers tend to drive with the desirable speed, however individual driving behavior configured and restricted by the prevailing traffic conditions (leading and adjacent vehicles, traffic control systems, congestion etc.) (Transport Simulation Systems, 2014). AIMSUN uses Gipps car following model. As mentioned above, traffic microsimulation models are based on the explicit representation of the individual driver behavior and individual vehicle real space-time trajectories. They described as vector that consist of co-ordinates such as the size of the vehicle, speed, maximum acceleration and vehicle location. AIMSUN use, not only a traffic simulation model but also a vehicle emission model, so it can calculate emission pollutants produced by the vehicle. The pollutants modelled are: nitrogen oxides (NOx), volatile organic compounds (VOC), carbon dioxide (CO2) and particulate matter (PM). These particular pollutants were chosen based on their potential health impacts and external cost imposed to the society. In addition carbon dioxide is modelled because of its effect on global climate change and its immediate link to fuel consumption (Panis L. et all., 2006). 3. Study area Thessaloniki is the second largest city in Greece and is located in the northern part of the country. Thessaloniki’s ring road is a major arterial connecting the suburban areas bypass the city center. More specifically, the area studied includes a section, 14 km in length, of Thessaloniki’s inner ring road – Eastbound, which incorporates three main junctions connecting ring road with the outer area. Inner ring road is a dual – carriageway suburban road with 3 – lanes per direction (Figure 1).
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Figure 1: Exact study area
Coding of the road section together with its nearby connectors was carried out in order to perform the simulation. As a result, a number of 243 sections and 119 nodes, 3 of which are signalized, are inserted in the model. Coding of road sections was based on the road type and the geometric and traffic characteristics such as the number of lanes and the direction of traffic. Four different road types were defined as follows:
Suburban arterial - Ring Road (maximum speed 100 km/h) On/Off ramp (maximum speed 60km/h) Secondary urban road (maximum speed 60km/h) Signalized street (maximum speed 50km/h)
Traffic volumes introduced to the model were derived as results from the elaboration of a macroscopic traffic assignment model that was developed for the city greater area by the Hellenic Institute of Transport using real traffic counts for the morning period 10.00-11.00 of weekday. Finally, the traffic assignment model used in this research work is a static stochastic model. In this specific type of model, it is considered that drivers have incomplete knowledge about traffic conditions of the network and their drive behavior isn’t rational economically. 4. Development of scenarios Coding and calibration of the network were followed by the formation and the evaluation of the different scenarios by the use of Aimsun. There was an effort so that the selected scenarios will represent realistic road events, relate to a
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wide range of traffic conditions. Traffic composition observed consisted of 90% cars, 8% commercial vehicles and 2% taxis. For the development of scenarios traffic demand was defined as “low” (-50% of the basic), “basic” and “high” (+50% of the basic) and also incident duration was considered to be 25 and 50 minutes. All the same, the incident impacts were examined for two different event locations that were near from a junction (node) ad far from a junction (node). Two restrictions imposed to the simulation process include the length of 100m of the road lane blocked by the accidents in the area of the incident and the reduction of the vehicle approach speed to the incident location by 15 km/h, 20km/h and 50 km/h, depending on the road lane and the distance from the exact incident location. The scenarios with the corresponding characteristics of the road incidents they relate to are presented in the following table. Table 4.1: Characteristics of the scenarios SCENARIOS
LOCATION Near to a Node
DURATION (MINS)
Far from a node
25'
1st
Scenario 2nd
3rd
Scenario 4th
Scenario 5th
Scenario 6th
Scenario Scenario
7th
Scenario
Scenario 8th
9th
Scenario
50'
DEMAND Low Demand
Basic Demand
High Demand
Scenario 10th
Scenario 11th
Scenario 12th
5. Evaluation of traffic and environmental impacts The impact assessment analysis was performed at a local and at the network level. Local level is defined as the area near the incident where the effects are direct and more intense. Regarding the network level analysis, it provides a more comprehensive approach of the incident impacts and also allows the calculation of the environmental impacts throughout the network. The selected indicators concerning the operational performance that were examined include: travel time, traffic volume (vehicle/hour), delays per vehicle, queue length (vehicles/lane), speed (km/h), density and air pollution emissions (CO2, NOx, VOC and PM). Operational performance indicators were considered in terms of mean, maximum and total values. Also, the variation in time of these values was examined during the entire simulation period which was defined to be one hour.
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5.1. Local level analysis results
a
b Figure 2: (a) Average delay time for the scenarios 1-6, (b) Average delay time for the scenarios 7-12
Figure 2 demonstrates that the maximum average delays occur when demand reaches its maximum values. Also that average delay is higher for the scenarios 7-12 that have been developed regarding incident location near the node. Figure 3 shows that when the incident locates far from the node and its duration is 25 min, then the maximum delay time occurred at high level demand and at the end of the event. It was also found when the duration of the event is 50 min, then the maximum average delay time occurred at almost the end of the simulation period.
Figure 3: Average delay time for traffic incident far from a node and duration 25 minutes
Figure 4 demonstrates the average speed values observed for the 12 scenarios with the lowest values to correspond to scenarios 11 and 12.
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70,66
Average speed
Average speed 66,94
58,06
64,99
62,75
59,13
2nd
3rd
4th
5th
6th
41,16
km/h
km/h
40,31
1st
a
293
7th
b
46,63 32,42
8th
9th
23,46
19,83
11th
12th
10th
Figure 4: (a) Average speed for the scenarios 1-6, (b) Average speed for the scenarios 7-12
Figure 5 demonstrates the average traffic volume values observed for the 12 scenarios with scenarios 8 and 9 to exhibit the largest values. Low traffic volumes characterize scenarios 11 and 12 due to the heavy congestion conditions prevailing in these cases. Also Figure 6 shows the medium and maximum density values for the 12 scenarios. Again scenarios 11 and 12 are characterized by the highest values.
Average traffic volume
Average traffic volume 903,5
1st
a
1386,8
1194,6
1289,4
1517,0
903,7
2nd
3rd
4th
vehicle/h
vehicle/h
1194,0
5th
6th
1746,0
1177,6
1173,5
7th
b
8th
9th
1332,0 1353,6
10th
11th
12th
Figure 5: (a) Average traffic volume for the scenarios 1-6, (b) Average traffic volume for the scenarios 7-12
Max and Average density
a
116,36
129,03
56,87 4,97 1st
113,88 124,50 49,30
8,61 2nd
14,72
6,03
3rd 4th Max of density
11,66 5th
vehicle/km
vehicle/km
Max and Average density
26,83 6th
145,68 149,86 137,80 150,72 150,14 94,88 10,47 7th
b
30,39 8th
38,05
18,14
9th 10th Max of density
44,96
11th
49,91
12th
Figure 6: (a) Max and average density for the scenarios 1-6, (b) Max and average density for the scenarios 7-12
Scenarios with the highest emission values are 6, 8, 9, 11 and 12 (Figure 7). The results of CO 2 emissions are identical in trend to the results for all the other emission pollutant values that were examined.
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CO2 emissions
CO2 emissions
35774533 ,1
5750712 5529419 4555339 6 5 3573155 7 2292626 2121325 5 1 5
kg
kg
28037459 ,5 21634628 19277872 ,8 ,1 12670188 11051204 ,4 ,0
1st
a
2nd
3rd
4th
5th
6th
7th
b
8th
9th
10th
11th
12th
Figure 7: (a) CO2 emissions for the scenarios 1-6, (b) CO2 emissions for the scenarios 7-12
5.2. Network level analysis results At the network level, Figure 8 demonstrates the min-med-max speed values for the 12 scenarios. Again speed is strongly dependent on the distance from the incident location and on the demand level.
Max-Average-Min speed
km/h
74,72 71,01 74,67 74,63 74,63 70,32 70,12 69,38 65,65 65,59 62,91 58,64 65,04 62,65 62,61 58,50 65,04 61,81 57,63 56,05 53,08 50,21 52,85 52,98 50,72 52,38 52,42 52,43 50,22 47,28 51,30 44,58 41,74 40,00 38,45 34,24
1st
2nd
3rd
4th 5th Max of speed
6th 7th Average of speed
8th 9th Min of speed
10th
11th
12th
Figure 8: Max- Average– Min speed for the scenarios 1-12
Figures 9 and 10 demonstrate the average values of traffic volumes and of travel time for the 12 scenarios. Again it is observed that high demand values relate to high values of the above mentioned indicators.
Average traffic volume
Average traffic volume 10732,0 10562,9 0 0 9364,60 9319,80 7545,70 7531,20
a
1st
2nd
3rd
4th
5th
6th
vehicle/h
vehicle/h
10537,3 10023,1 0 0 9259,00 8921,40 7548,70 7537,70
b
7th
8th
9th
10th
11th
12th
Figure 9: (a) Average traffic volume for the scenarios 1-6, (b) Average traffic volume for the scenarios 7-12
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Average travel time
Average travel time 1289039 997646
1004051
687398
683427
1st
2nd
3rd
a
4th
5th
6th
1379568
1325600 1046006
1039280 sec./km
sec./km
1323001
295
700343
687877
7th
8th
9th
10th
11th
12th
b
Figure 10: (a) Average travel time for the scenarios 1-6, (b) Average travel time for the scenarios 7-12
6. Conclusion From the analysis performed concerning the inner ring road of Thessaloniki the following outcomes are resulted. When the local level (ring road) is considered, demand level appears to be the main factor affecting travel time, delays, queue length and emissions far more than the duration of the incident. In all cases, the effects are more intense when the incident location is near a node (junction). Concerning the air pollution, NO X, CO2 and PM pollutants do not vary considerable for the same demand level among different incident duration. VOC organic components were found to be affected by both demand and incident duration. Examining the results from the analysis at the network level it is obvious that the variations in the values of indicators is considerably lower among the scenarios with different location and incident duration. However, it is the demand again which explains the majority of these variations. The outcomes of the research reveals the importance of the traffic management schemes implemented on main arterial road to handle traffic incidents and therefore to minimize traffic, economic and environmental effects of traffic incidents that could take place. Especially in the case of the Thessaloniki inner ring road, findings-reveal the importance of the need to provide an emergency lane. The absence of this lane increases the generalized operation cost of the suburban arterial especially when demand increases. References Charoniti E, 2013, ‘Analysis all the alternative scenarios for traffic event management through microscopic simulation’, Diploma Thesis, School of Civil Engineering, National Technical University of Athens. Giuliano G., 1998, “Incident Characteristics, Frequency, and Duration on a High Volume Urban Freeway”. Transportation Research Part A, Vol.23, pp. 387-396. Kyoungo A., 1998 ‘Microscopic Fuel Consumption and Emission Modelling’, M.Sc. Thesis, Faculty of Virginia Polytechnic Institute and State University. Levinson D. and Huo H., 2002, ‘Effectiveness of Variable Message Sign’, Transportation Research Board Conference, Washington DC (Session 825). Panis L., Broekx S., Liu R., 2006, ‘Modelling instantaneous traffic emission and the influence of traffic speed limits’, Science of the Total Environment 371, pp. 270-285. US. Department of Transportation. Traffic Incident Management Handbook. 2010. 1997 – 2004 TSS – Transport Simulation Systems, 2014, ‘Aimsun 8 Dynamic Simulators Users’ Manual’. ©OpenStreetMap contributors
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ScienceDirect
Available online at www.sciencedirect.com Transportation Transportation Research Research Procedia Procedia 00 00 (2016) (2016) 000–000 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 24C (2017) 288–295 www.elsevier.com/locate/procedia
3rd Conference on Sustainable Urban Mobility, 3rd CSUM 2016, 26 – 27 May 2016, Volos, Greece
Traffic and environmental impacts of traffic incidents on Thessaloniki’s inner ring road a*, Socrates Basbasbb, Evangelos Mintsiscc, George Mintsisbb, Eleni Kariotia* Christos Taxiltarisbb aa Faculty
54124, Thessaloniki, Thessaloniki, Greece Greece Faculty of of Civil Civil Engineering, Engineering, Aristotle Aristotle University University of of Thessaloniki, Thessaloniki, 54124, of of Rural Rural and and Surveying Surveying Engineering, Engineering, Aristotle Aristotle University University of of Thessaloniki, Thessaloniki, 54124, 54124, Thessaloniki, Thessaloniki, Greece Greece cc Center for Research and Technology Hellas - Hellenic Institute of Transport, Thermi, 57001,Thessaloniki, Greece Center for Research and Technology Hellas - Hellenic Institute of Transport, Thermi, 57001,Thessaloniki, Greece bb Faculty Faculty
Abstract Abstract This This paper paper examines examines the the traffic traffic and and the the associated associated environmental environmental impacts impacts of of traffic traffic incidents incidents with with the the use use of of the the traffic traffic microscopic microscopic simulation software the option option of of simulation software Aimsun. Aimsun. The The specific specific software software simulates simulates the the movement movement of of the the individual individual vehicles vehicles and and provides provides the simulating incidents incidents at at specific specific periods periods of of time, time, thus thus constituting constituting an an appropriate appropriate simulation simulation testbed. testbed. The The study study area area includes includes aa 14km 14km simulating section of of the the Thessaloniki’s Thessaloniki’s inner inner ring ring road. road. The The traffic traffic volumes volumes used used in in the the model model are are of of aa typical typical weekday weekday and and they they are are the the outputs outputs section of aa macroscopic macroscopic traffic traffic assignment assignment model model properly properly applied applied in in the the greater greater city city area. area. Traffic Traffic counts counts made made with with the the use use of of sens sens ors of ors in in the city city road road network network were were input input in in the the traffic traffic assignment assignment model. model. Different Different scenarios scenarios of of possible possible traffic traffic incidents incidents on on the the inner inner ring ring the road in in terms terms of of location location and and duration duration were were developed developed and and evaluated evaluated using using Aimsun. Aimsun. The The impacts impacts of of defined defined incidents incidents to to the the road performance of of the the ring ring road road were were examined examined with with emphasis emphasis to to the the near near and and outer outer area area of of incident incident position. position. For For the the evaluation evaluation performance process two two different different time time periods periods (25 (25 and and 50 50 mins) mins) and and three three different different demand demand levels levels (low, (low, base-case, base-case, and and high) high) were were examined. examined. process Thus, 12 12 different different scenarios scenarios were were defined defined and and tested tested with with the the traffic traffic microscopic microscopic simulation simulation software. software. The The indicators indicators used used in in the the Thus, evaluation process process include, include, among among others, others, traffic traffic density, density, mean mean network network speed, speed, vehicle vehicle –– kilometers evaluation kilometers travelled, travelled, fuel fuel consumption, consumption, CO CO22 emissions emissions etc. etc. Results Results have have shown shown that that traffic traffic speed speed and and delays delays are are seriously seriously affected affected when when incidents incidents occur occur near near merging merging or or diverging diverging areas areas in in the the inner inner ring ring road. road. At At the the same same time time incident incident duration duration and and traffic traffic demand demand deteriorated deteriorated as as traffic traffic conditi conditi ons ons increased in in values. values. increased © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The under Authors. Published by Elsevier B.V. committee of the 3rd CSUM 2016. Peer-review responsibility of the organizing Peer-review under responsibility of the organizing Peer-review under responsibility of the organizing committee committee of of the the 3rd 3rd CSUM CSUM 2016. 2016. Keywords: traffic simulation, simulation, miscroscopic miscroscopic simulation simulation model, model, incidents incidents management, management, Thessaloniki’s Thessaloniki’s ring ring road, road, AIMSUN AIMSUN Keywords: traffic * E-mail address: address: [email protected] [email protected] * Corresponding Corresponding author. author. Tel.: Tel.: +30-698-271-1907. +30-698-271-1907. E-mail
1. Introduction A traffic incident is defined as any non-recurring event that causes a reduction of roadway capacity or an unusual increase in demand. They resulted from crashes, vehicles broke down and spilled cargo (US. Department of Transportation, 2010). Incidents are responsible for a variety of impacts both on traffic conditions and the environment.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 3rd CSUM 2016. 10.1016/j.trpro.2017.05.120
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Traffic incidents have long been recognized as the main contributor of congestion in road networks. Incidents cause approximately 25% of traffic congestion, and even a higher proportion for urban expressways (Giuliano G, 1989).Furthermore, extended research has showed that there is a strong relationship between the traffic congestion in an incident situation and the vehicle fuel consumption combined with the pollutants emitted (CO, CO2, NOX, HC, and Pb) (Kyoungho A., 1998, Levinson D. and Huo H. 2002). Additionally, the vehicles delays on the road network caused by incidents, cost in terms of money and time. Secondary traffic incidents at a road incidents area are observed in many cases due to increased traffic hazard created by the prevailing traffic congestion. Considering the above impacts it is clearly perceived that traffic incidents generate a significant socio-economic and environment cost. The research work described in this paper attempts to evaluate impacts caused to traffic and the environment by road incidents occurring at different locations and with different duration and demand conditions on the inner ring road of Thessaloniki. The study area includes a 14km section of the ring road and the tool used in order to perform the evaluation was the traffic microscopic simulation software AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks). 2. Microscopic traffic simulation Microscopic simulation models identify the individual movements of each vehicle and are based on their description by a vector which consists of coordinates such as vehicle’s size, speed, maximum acceleration and its position. The advantage of this type of simulation is the detailing description of traffic conditions because of the wide range of vehicles movement characteristics and road conditions that can be simulated (Charoniti E., 2013). Analyzing the microscopic traffic simulation closely, most of the models use various algorithms and models of driving behavior to simulate the movement of individual vehicle on the network. The three basic algorithm are car – following, lane – changing and gap – acceptance. Within Aimsun the interactions between vehicles are examined using a car-following and a lane – changing algorithm. Drivers tend to drive with the desirable speed, however individual driving behavior configured and restricted by the prevailing traffic conditions (leading and adjacent vehicles, traffic control systems, congestion etc.) (Transport Simulation Systems, 2014). AIMSUN uses Gipps car following model. As mentioned above, traffic microsimulation models are based on the explicit representation of the individual driver behavior and individual vehicle real space-time trajectories. They described as vector that consist of co-ordinates such as the size of the vehicle, speed, maximum acceleration and vehicle location. AIMSUN use, not only a traffic simulation model but also a vehicle emission model, so it can calculate emission pollutants produced by the vehicle. The pollutants modelled are: nitrogen oxides (NOx), volatile organic compounds (VOC), carbon dioxide (CO2) and particulate matter (PM). These particular pollutants were chosen based on their potential health impacts and external cost imposed to the society. In addition carbon dioxide is modelled because of its effect on global climate change and its immediate link to fuel consumption (Panis L. et all., 2006). 3. Study area Thessaloniki is the second largest city in Greece and is located in the northern part of the country. Thessaloniki’s ring road is a major arterial connecting the suburban areas bypass the city center. More specifically, the area studied includes a section, 14 km in length, of Thessaloniki’s inner ring road – Eastbound, which incorporates three main junctions connecting ring road with the outer area. Inner ring road is a dual – carriageway suburban road with 3 – lanes per direction (Figure 1).
2
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Figure 1: Exact study area
Coding of the road section together with its nearby connectors was carried out in order to perform the simulation. As a result, a number of 243 sections and 119 nodes, 3 of which are signalized, are inserted in the model. Coding of road sections was based on the road type and the geometric and traffic characteristics such as the number of lanes and the direction of traffic. Four different road types were defined as follows:
Suburban arterial - Ring Road (maximum speed 100 km/h) On/Off ramp (maximum speed 60km/h) Secondary urban road (maximum speed 60km/h) Signalized street (maximum speed 50km/h)
Traffic volumes introduced to the model were derived as results from the elaboration of a macroscopic traffic assignment model that was developed for the city greater area by the Hellenic Institute of Transport using real traffic counts for the morning period 10.00-11.00 of weekday. Finally, the traffic assignment model used in this research work is a static stochastic model. In this specific type of model, it is considered that drivers have incomplete knowledge about traffic conditions of the network and their drive behavior isn’t rational economically. 4. Development of scenarios Coding and calibration of the network were followed by the formation and the evaluation of the different scenarios by the use of Aimsun. There was an effort so that the selected scenarios will represent realistic road events, relate to a
3
Eleni Karioti et al. / Transportation Research Procedia 24C (2017) 288–295
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wide range of traffic conditions. Traffic composition observed consisted of 90% cars, 8% commercial vehicles and 2% taxis. For the development of scenarios traffic demand was defined as “low” (-50% of the basic), “basic” and “high” (+50% of the basic) and also incident duration was considered to be 25 and 50 minutes. All the same, the incident impacts were examined for two different event locations that were near from a junction (node) ad far from a junction (node). Two restrictions imposed to the simulation process include the length of 100m of the road lane blocked by the accidents in the area of the incident and the reduction of the vehicle approach speed to the incident location by 15 km/h, 20km/h and 50 km/h, depending on the road lane and the distance from the exact incident location. The scenarios with the corresponding characteristics of the road incidents they relate to are presented in the following table. Table 4.1: Characteristics of the scenarios SCENARIOS
LOCATION Near to a Node
DURATION (MINS)
Far from a node
25'
1st
Scenario 2nd
3rd
Scenario 4th
Scenario 5th
Scenario 6th
Scenario Scenario
7th
Scenario
Scenario 8th
9th
Scenario
50'
DEMAND Low Demand
Basic Demand
High Demand
Scenario 10th
Scenario 11th
Scenario 12th
5. Evaluation of traffic and environmental impacts The impact assessment analysis was performed at a local and at the network level. Local level is defined as the area near the incident where the effects are direct and more intense. Regarding the network level analysis, it provides a more comprehensive approach of the incident impacts and also allows the calculation of the environmental impacts throughout the network. The selected indicators concerning the operational performance that were examined include: travel time, traffic volume (vehicle/hour), delays per vehicle, queue length (vehicles/lane), speed (km/h), density and air pollution emissions (CO2, NOx, VOC and PM). Operational performance indicators were considered in terms of mean, maximum and total values. Also, the variation in time of these values was examined during the entire simulation period which was defined to be one hour.
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5.1. Local level analysis results
a
b Figure 2: (a) Average delay time for the scenarios 1-6, (b) Average delay time for the scenarios 7-12
Figure 2 demonstrates that the maximum average delays occur when demand reaches its maximum values. Also that average delay is higher for the scenarios 7-12 that have been developed regarding incident location near the node. Figure 3 shows that when the incident locates far from the node and its duration is 25 min, then the maximum delay time occurred at high level demand and at the end of the event. It was also found when the duration of the event is 50 min, then the maximum average delay time occurred at almost the end of the simulation period.
Figure 3: Average delay time for traffic incident far from a node and duration 25 minutes
Figure 4 demonstrates the average speed values observed for the 12 scenarios with the lowest values to correspond to scenarios 11 and 12.
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70,66
Average speed
Average speed 66,94
58,06
64,99
62,75
59,13
2nd
3rd
4th
5th
6th
41,16
km/h
km/h
40,31
1st
a
293
7th
b
46,63 32,42
8th
9th
23,46
19,83
11th
12th
10th
Figure 4: (a) Average speed for the scenarios 1-6, (b) Average speed for the scenarios 7-12
Figure 5 demonstrates the average traffic volume values observed for the 12 scenarios with scenarios 8 and 9 to exhibit the largest values. Low traffic volumes characterize scenarios 11 and 12 due to the heavy congestion conditions prevailing in these cases. Also Figure 6 shows the medium and maximum density values for the 12 scenarios. Again scenarios 11 and 12 are characterized by the highest values.
Average traffic volume
Average traffic volume 903,5
1st
a
1386,8
1194,6
1289,4
1517,0
903,7
2nd
3rd
4th
vehicle/h
vehicle/h
1194,0
5th
6th
1746,0
1177,6
1173,5
7th
b
8th
9th
1332,0 1353,6
10th
11th
12th
Figure 5: (a) Average traffic volume for the scenarios 1-6, (b) Average traffic volume for the scenarios 7-12
Max and Average density
a
116,36
129,03
56,87 4,97 1st
113,88 124,50 49,30
8,61 2nd
14,72
6,03
3rd 4th Max of density
11,66 5th
vehicle/km
vehicle/km
Max and Average density
26,83 6th
145,68 149,86 137,80 150,72 150,14 94,88 10,47 7th
b
30,39 8th
38,05
18,14
9th 10th Max of density
44,96
11th
49,91
12th
Figure 6: (a) Max and average density for the scenarios 1-6, (b) Max and average density for the scenarios 7-12
Scenarios with the highest emission values are 6, 8, 9, 11 and 12 (Figure 7). The results of CO 2 emissions are identical in trend to the results for all the other emission pollutant values that were examined.
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294
CO2 emissions
CO2 emissions
35774533 ,1
5750712 5529419 4555339 6 5 3573155 7 2292626 2121325 5 1 5
kg
kg
28037459 ,5 21634628 19277872 ,8 ,1 12670188 11051204 ,4 ,0
1st
a
2nd
3rd
4th
5th
6th
7th
b
8th
9th
10th
11th
12th
Figure 7: (a) CO2 emissions for the scenarios 1-6, (b) CO2 emissions for the scenarios 7-12
5.2. Network level analysis results At the network level, Figure 8 demonstrates the min-med-max speed values for the 12 scenarios. Again speed is strongly dependent on the distance from the incident location and on the demand level.
Max-Average-Min speed
km/h
74,72 71,01 74,67 74,63 74,63 70,32 70,12 69,38 65,65 65,59 62,91 58,64 65,04 62,65 62,61 58,50 65,04 61,81 57,63 56,05 53,08 50,21 52,85 52,98 50,72 52,38 52,42 52,43 50,22 47,28 51,30 44,58 41,74 40,00 38,45 34,24
1st
2nd
3rd
4th 5th Max of speed
6th 7th Average of speed
8th 9th Min of speed
10th
11th
12th
Figure 8: Max- Average– Min speed for the scenarios 1-12
Figures 9 and 10 demonstrate the average values of traffic volumes and of travel time for the 12 scenarios. Again it is observed that high demand values relate to high values of the above mentioned indicators.
Average traffic volume
Average traffic volume 10732,0 10562,9 0 0 9364,60 9319,80 7545,70 7531,20
a
1st
2nd
3rd
4th
5th
6th
vehicle/h
vehicle/h
10537,3 10023,1 0 0 9259,00 8921,40 7548,70 7537,70
b
7th
8th
9th
10th
11th
12th
Figure 9: (a) Average traffic volume for the scenarios 1-6, (b) Average traffic volume for the scenarios 7-12
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Average travel time
Average travel time 1289039 997646
1004051
687398
683427
1st
2nd
3rd
a
4th
5th
6th
1379568
1325600 1046006
1039280 sec./km
sec./km
1323001
295
700343
687877
7th
8th
9th
10th
11th
12th
b
Figure 10: (a) Average travel time for the scenarios 1-6, (b) Average travel time for the scenarios 7-12
6. Conclusion From the analysis performed concerning the inner ring road of Thessaloniki the following outcomes are resulted. When the local level (ring road) is considered, demand level appears to be the main factor affecting travel time, delays, queue length and emissions far more than the duration of the incident. In all cases, the effects are more intense when the incident location is near a node (junction). Concerning the air pollution, NO X, CO2 and PM pollutants do not vary considerable for the same demand level among different incident duration. VOC organic components were found to be affected by both demand and incident duration. Examining the results from the analysis at the network level it is obvious that the variations in the values of indicators is considerably lower among the scenarios with different location and incident duration. However, it is the demand again which explains the majority of these variations. The outcomes of the research reveals the importance of the traffic management schemes implemented on main arterial road to handle traffic incidents and therefore to minimize traffic, economic and environmental effects of traffic incidents that could take place. Especially in the case of the Thessaloniki inner ring road, findings-reveal the importance of the need to provide an emergency lane. The absence of this lane increases the generalized operation cost of the suburban arterial especially when demand increases. References Charoniti E, 2013, ‘Analysis all the alternative scenarios for traffic event management through microscopic simulation’, Diploma Thesis, School of Civil Engineering, National Technical University of Athens. Giuliano G., 1998, “Incident Characteristics, Frequency, and Duration on a High Volume Urban Freeway”. Transportation Research Part A, Vol.23, pp. 387-396. Kyoungo A., 1998 ‘Microscopic Fuel Consumption and Emission Modelling’, M.Sc. Thesis, Faculty of Virginia Polytechnic Institute and State University. Levinson D. and Huo H., 2002, ‘Effectiveness of Variable Message Sign’, Transportation Research Board Conference, Washington DC (Session 825). Panis L., Broekx S., Liu R., 2006, ‘Modelling instantaneous traffic emission and the influence of traffic speed limits’, Science of the Total Environment 371, pp. 270-285. US. Department of Transportation. Traffic Incident Management Handbook. 2010. 1997 – 2004 TSS – Transport Simulation Systems, 2014, ‘Aimsun 8 Dynamic Simulators Users’ Manual’. ©OpenStreetMap contributors
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