Vehicular federated learning systems will be beneficial to predicting traffic events in future intelligent cities. However, they might leak private information upon model updates. Hence, an honest but curious server could infer private information, such as the route of a vehicle. In this study, we elaborate on the nature of such privacy leakage caused by gradient sharing. With a simulated scenario, we focus on determining who is in danger of privacy threats and how successful a route inference attack can be. Results indicate that vanilla federated learning exposes intra-city and commuter traffic to successful location inference attacks. We also found that an adversarial aggregator server successfully infers the moving time of vehicles traveling during low-traffic periods.
{"title":"On Vehicular Data Aggregation in Federated Learning","authors":"Levente Alekszejenkó, Tadeusz Dobrowiecki","doi":"10.52825/scp.v5i.1100","DOIUrl":"https://doi.org/10.52825/scp.v5i.1100","url":null,"abstract":"Vehicular federated learning systems will be beneficial to predicting traffic events in future intelligent cities. However, they might leak private information upon model updates. Hence, an honest but curious server could infer private information, such as the route of a vehicle. In this study, we elaborate on the nature of such privacy leakage caused by gradient sharing. With a simulated scenario, we focus on determining who is in danger of privacy threats and how successful a route inference attack can be.\u0000Results indicate that vanilla federated learning exposes intra-city and commuter traffic to successful location inference attacks. We also found that an adversarial aggregator server successfully infers the moving time of vehicles traveling during low-traffic periods.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Max Schrader, Arya Karnik, Alexander Hainen, Joshua A. Bittle
This paper presents an innovative calibration method for car-following (CF) models in the Simulation of Urban MObility (SUMO) using real-world trajectory data from a 1.5 km signalized urban corridor, captured by roadside radars. By applying a sophisticated track-level association and fusion methodology, the study extends trajectory analysis beyond individual radar fields of view. The enhanced data is then utilized to refine the Krauss, IDM, and W99 CF models within SUMO, addressing the literature gap by integrating SUMO into the calibration loop, thereby accommodating the simulator's integration scheme and any model adaptations. The research identifies that default SUMO models tend to exhibit shorter time headways compared to real-world data, with calibration effectively reducing this discrepancy. Moreover, the W99 model, despite its unrealistic acceleration profiles when calibrated without considering acceleration, most accurately captures the higher-end energy consumption distribution. Conversely, the IDM model, with its default parameters, provides the closest approximation to observed acceleration behaviors, highlighting the nuanced performance of CF models in traffic simulation and their implications for energy consumption estimation. Detailed results of optimized parameters for each CF model are provided in appendix in addition to distribution information that may be useful for other modelers to use directly or other datasets to be compared in the future (including expansion of the work to include vehicle classification).
{"title":"Calibrating Car-Following Models Using SUMO-in-the-Loop and Vehicle Trajectories From Roadside Radar","authors":"Max Schrader, Arya Karnik, Alexander Hainen, Joshua A. Bittle","doi":"10.52825/scp.v5i.1127","DOIUrl":"https://doi.org/10.52825/scp.v5i.1127","url":null,"abstract":"This paper presents an innovative calibration method for car-following (CF) models in the Simulation of Urban MObility (SUMO) using real-world trajectory data from a 1.5 km signalized urban corridor, captured by roadside radars. By applying a sophisticated track-level association and fusion methodology, the study extends trajectory analysis beyond individual radar fields of view. The enhanced data is then utilized to refine the Krauss, IDM, and W99 CF models within SUMO, addressing the literature gap by integrating SUMO into the calibration loop, thereby accommodating the simulator's integration scheme and any model adaptations. The research identifies that default SUMO models tend to exhibit shorter time headways compared to real-world data, with calibration effectively reducing this discrepancy. Moreover, the W99 model, despite its unrealistic acceleration profiles when calibrated without considering acceleration, most accurately captures the higher-end energy consumption distribution. Conversely, the IDM model, with its default parameters, provides the closest approximation to observed acceleration behaviors, highlighting the nuanced performance of CF models in traffic simulation and their implications for energy consumption estimation. Detailed results of optimized parameters for each CF model are provided in appendix in addition to distribution information that may be useful for other modelers to use directly or other datasets to be compared in the future (including expansion of the work to include vehicle classification).","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The UNECE regulation R157 [1] describes the requirements for a successful implementation of an approvable ALKS (Automated Lane-Keeping System) in great detail. This paper reviews some of the content of this document and describes the first steps that would be needed on how to implement such an ALKS as another driver model into the open source microscopic traffic flow simulator SUMO.
{"title":"Perspectives on an ALKS Model in SUMO","authors":"Robert Alms, Benjamin Couéraud, Peter Wagner","doi":"10.52825/scp.v5i.1198","DOIUrl":"https://doi.org/10.52825/scp.v5i.1198","url":null,"abstract":"The UNECE regulation R157 [1] describes the requirements for a successful implementation of an approvable ALKS (Automated Lane-Keeping System) in great detail. This paper reviews some of the content of this document and describes the first steps that would be needed on how to implement such an ALKS as another driver model into the open source microscopic traffic flow simulator SUMO.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alvaro Paricio-Garcia, Miguel A. López-Carmona, Pablo Manglano-Redondo
Focusing on the critical challenge of air pollution in urban areas, primarily caused by vehicular emissions, this study proposes an innovative process for Low Emission Zones (LEZ) design within the Simulation of Urban Mobility (SUMO) framework. Our primary focus is on enhancing urban mobility through the strategic design of LEZs, while simultaneously maintaining or even improving emission levels. The novel aspect of the approach lies in the use of LEZs with minimal geometric boundaries, strategically designed to balance the reduction of CO2 emissions and the necessity of fluid urban transportation. LEZs are calculated using genetic algorithms that optimize a cost function balancing emissions and travel time while applying topological and specific constraints. Several experiments are simulated with SUMO to compare the efficiency of urban mobility under various LEZ configurations and different traffic demands. The results show the improvement of the approach in comparison with traditional LEZ design methodologies. The proposal not only preserves or even reduces the emission levels, but also actively improves urban mobility and traffic flow. This empirical evidence strongly supports the feasibility and effectiveness of the proposed solution in different urban scenarios. The design of the heuristic enables the possibility to create dynamic LEZs that may be changed depending on demand, weather, or any other varying conditions that affect traffic and emissions, preserving the mobility concerns of the users.
{"title":"Optimized Design of Low Emission Zones in SUMO","authors":"Alvaro Paricio-Garcia, Miguel A. López-Carmona, Pablo Manglano-Redondo","doi":"10.52825/scp.v5i.1143","DOIUrl":"https://doi.org/10.52825/scp.v5i.1143","url":null,"abstract":"Focusing on the critical challenge of air pollution in urban areas, primarily caused by vehicular emissions, this study proposes an innovative process for Low Emission Zones (LEZ) design within the Simulation of Urban Mobility (SUMO) framework. Our primary focus is on enhancing urban mobility through the strategic design of LEZs, while simultaneously maintaining or even improving emission levels. The novel aspect of the approach lies in the use of LEZs with minimal geometric boundaries, strategically designed to balance the reduction of CO2 emissions and the necessity of fluid urban transportation. LEZs are calculated using genetic algorithms that optimize a cost function balancing emissions and travel time while applying topological and specific constraints. Several experiments are simulated with SUMO to compare the efficiency of urban mobility under various LEZ configurations and different traffic demands. The results show the improvement of the approach in comparison with traditional LEZ design methodologies. The proposal not only preserves or even reduces the emission levels, but also actively improves urban mobility and traffic flow. This empirical evidence strongly supports the feasibility and effectiveness of the proposed solution in different urban scenarios. The design of the heuristic enables the possibility to create dynamic LEZs that may be changed depending on demand, weather, or any other varying conditions that affect traffic and emissions, preserving the mobility concerns of the users.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.
{"title":"Joining SUMO and Unreal Engine to Create a Bespoke 360 Degree Narrow Passage Driving Simulator","authors":"Peter Youssef, Katherine L. Plant, Ben Waterson","doi":"10.52825/scp.v5i.1104","DOIUrl":"https://doi.org/10.52825/scp.v5i.1104","url":null,"abstract":"The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation.\u0000Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katia Juliane Schmidt, Natalie Steinmetz, Martin Margreiter
This study proposes a bus prioritisation strategy at signalised intersections to enhance public transport reliability and attractiveness. Nowadays, bus prioritisation at intersections is conducted according to a first-come, first-served principle, lacking compatibility with future vehicle-to-infrastructure communication. A framework for prioritising buses based on their delay and occupancy was developed and tested in a SUMO microscopic traffic simulation subnetwork of the city of Ingolstadt. Buses are prioritised using a 25-level hierarchy. Four degrees of prioritisation interventions are implemented based on bus priority levels with signal cycles adjusted to advance preferred green phases. The timing of the prioritisation is based on an Estimated Time of Arrival (ETA) prediction that considers past speed and travel time data as well as bus stops that are on the way to the intersection. The prioritisation logic was tested in simulation scenarios with on- and off-peak conditions and with several buses requesting priority and varying degrees of priority. The results show that the developed prioritisation concept works, and prioritised buses benefit from a strong reduction in their travel times (up to 87 %) and number of stops. Buses with lower priority levels may experience deterioration in their travel time (up to 126 %) when arriving at the same time as a high-priority bus, but considering the fewer affected passengers and smaller delay, this seems acceptable.
{"title":"Bus Priority Procedure for Signalized Intersections Based on Bus Occupancy and Delay","authors":"Katia Juliane Schmidt, Natalie Steinmetz, Martin Margreiter","doi":"10.52825/scp.v5i.1111","DOIUrl":"https://doi.org/10.52825/scp.v5i.1111","url":null,"abstract":"This study proposes a bus prioritisation strategy at signalised intersections to enhance public transport reliability and attractiveness. Nowadays, bus prioritisation at intersections is conducted according to a first-come, first-served principle, lacking compatibility with future vehicle-to-infrastructure communication. A framework for prioritising buses based on their delay and occupancy was developed and tested in a SUMO microscopic traffic simulation subnetwork of the city of Ingolstadt. Buses are prioritised using a 25-level hierarchy. Four degrees of prioritisation interventions are implemented based on bus priority levels with signal cycles adjusted to advance preferred green phases. The timing of the prioritisation is based on an Estimated Time of Arrival (ETA) prediction that considers past speed and travel time data as well as bus stops that are on the way to the intersection. The prioritisation logic was tested in simulation scenarios with on- and off-peak conditions and with several buses requesting priority and varying degrees of priority. The results show that the developed prioritisation concept works, and prioritised buses benefit from a strong reduction in their travel times (up to 87 %) and number of stops. Buses with lower priority levels may experience deterioration in their travel time (up to 126 %) when arriving at the same time as a high-priority bus, but considering the fewer affected passengers and smaller delay, this seems acceptable.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix Wachter, Jannik Krivohlavek, Jonas Rossa, Andreas Rupp
At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible. The leading use case for drone applications is currently seen in the medical sector. Individual evidence shows that the use of drones to transport medical personnel for first medical response brings significant improvements in terms of cost and response time. Advantages in urban applications are seen as promising by some research projects, but this is not scientifically proven yet. This study deals with the simulation of transportation times of medical proposals by ambulance and drone in the metropolitan region of Stavanger and the comparison of transport times. The proposed methodology develops transferable results from concrete use-cases. By using a drone 80% of the expected operations can benefit from a reduction in transport time of up to 10 minutes with a variation of +/- 4 minutes through adjusted flight speeds due to weather conditions and variations in ambulance travel times due to different traffic volumes. The data set cleaned for the local special cases shows potential for a reduction of up to 20 minutes for the remaining operations, while the extracted individual cases even showing improvements of up to 60 minutes.
{"title":"Generalistic Assessments of the Potential of Medical Drones in Urban Environment","authors":"Felix Wachter, Jannik Krivohlavek, Jonas Rossa, Andreas Rupp","doi":"10.52825/scp.v5i.1056","DOIUrl":"https://doi.org/10.52825/scp.v5i.1056","url":null,"abstract":"At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible. The leading use case for drone applications is currently seen in the medical sector. Individual evidence shows that the use of drones to transport medical personnel for first medical response brings significant improvements in terms of cost and response time. Advantages in urban applications are seen as promising by some research projects, but this is not scientifically proven yet. This study deals with the simulation of transportation times of medical proposals by ambulance and drone in the metropolitan region of Stavanger and the comparison of transport times. The proposed methodology develops transferable results from concrete use-cases. By using a drone 80% of the expected operations can benefit from a reduction in transport time of up to 10 minutes with a variation of +/- 4 minutes through adjusted flight speeds due to weather conditions and variations in ambulance travel times due to different traffic volumes. The data set cleaned for the local special cases shows potential for a reduction of up to 20 minutes for the remaining operations, while the extracted individual cases even showing improvements of up to 60 minutes.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Klavdiya Olegovna Bochenina, Anton Taleiko, L. Ruotsalainen
Estimation of a travel demand in a form of origin-destination (OD) matrix is a necessary step in a city-scale simulation of the vehicular mobility. However, an input data on travel demand in OD matrix may be available only for a specific set of traffic assignment zones (TAZs). Thus, there appears a need to infer OD matrix for a region of interest (we call it ‘core’ area) given OD matrix for a larger region (we call it ‘extended’ area), which is challenging as trip counts are only given for zones of the initial region. To perform a reduction, we explicitly simulate vehicle trajectories for the extended area and supplement trip values in ‘core’ TAZs based on the recorded trajectories on the border of core and extended areas. To keep validation results consistent between extended and core simulations, we introduce edge-based origin-destination assignment algorithm which preserves properties of traffic flows on the border of the core area but also keeps randomness in instantiating simulation for the core area. The experimental study is performed for Helsinki city area using Simulation of Urban MObility (SUMO) tool. The validation was performed using DigiTraffic data from traffic counting stations within the city area for workdays of autumn 2018. Validation results show that the reduced OD matrix combined with edge-based OD assignment algorithm keeps the simulated traffic counts in good agreement with results from the extended area simulation with average MAPE between observed and simulated traffic counts equal to 34%. Simulation time after reduction is equal to 20 minutes compared to 6 hours for the extended OD.
{"title":"Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area","authors":"Klavdiya Olegovna Bochenina, Anton Taleiko, L. Ruotsalainen","doi":"10.52825/scp.v4i.197","DOIUrl":"https://doi.org/10.52825/scp.v4i.197","url":null,"abstract":"Estimation of a travel demand in a form of origin-destination (OD) matrix is a necessary step in a city-scale simulation of the vehicular mobility. However, an input data on travel demand in OD matrix may be available only for a specific set of traffic assignment zones (TAZs). Thus, there appears a need to infer OD matrix for a region of interest (we call it ‘core’ area) given OD matrix for a larger region (we call it ‘extended’ area), which is challenging as trip counts are only given for zones of the initial region. To perform a reduction, we explicitly simulate vehicle trajectories for the extended area and supplement trip values in ‘core’ TAZs based on the recorded trajectories on the border of core and extended areas. To keep validation results consistent between extended and core simulations, we introduce edge-based origin-destination assignment algorithm which preserves properties of traffic flows on the border of the core area but also keeps randomness in instantiating simulation for the core area. \u0000The experimental study is performed for Helsinki city area using Simulation of Urban MObility (SUMO) tool. The validation was performed using DigiTraffic data from traffic counting stations within the city area for workdays of autumn 2018. Validation results show that the reduced OD matrix combined with edge-based OD assignment algorithm keeps the simulated traffic counts in good agreement with results from the extended area simulation with average MAPE between observed and simulated traffic counts equal to 34%. Simulation time after reduction is equal to 20 minutes compared to 6 hours for the extended OD.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traffic simulators rely on calibrated driver models in order to reproduce human behavior in different traffic scenarios. Even if quite accurate results can be obtained, the actual interaction between human being and traffic cannot be completely reproduced. In particular, as automated vehicles are being developed, the human in the loop is required to understand whether drivers feel comfortable and safe in mixed traffic conditions. In recent years, dynamic driving simulators have been developed to test vehicles in complex or dangerous situations in safe and controlled environments. However, driving simulators are mostly devoted to the study of vehicle dynamics more than traffic situations.This paper presents an integration of SUMO with a high end dynamic driving simulator with the aim to study human reactions while negotiating a roundabout in mixed traffic conditions. SUMO is in charge of traffic simulation, while a full vehicle model is employed for the simulation of the dynamic of the human driven car. To allow a human to effectively drive the car, both simulation environments have to run in real time while exchanging the required information. Also, scenario graphics, sound and driving simulator feedback motion have to be accurately realized and synchronized with the simulations. A real-time server is employed for the synchronization of the different environments. As SUMO does not consider vehicle dynamics, particular attention is devoted to the a realistic reconstruction of trajectories and vehicle dynamics to be represented in the scenario. Some preliminary tests are shown where a panel of testers has been asked to negotiate the roundabout with different percentages of automated vehicles. The results of the tests show that drivers were able to perceive differences in the behavior of other vehicles and that the proposed approach is effective for understanding the feeling of comfort and safety of the human driver.
{"title":"SUMO Roundabout Simulation with Human in the Loop","authors":"G. Previati, G. Mastinu","doi":"10.52825/scp.v4i.211","DOIUrl":"https://doi.org/10.52825/scp.v4i.211","url":null,"abstract":"Traffic simulators rely on calibrated driver models in order to reproduce human behavior in different traffic scenarios. Even if quite accurate results can be obtained, the actual interaction between human being and traffic cannot be completely reproduced. In particular, as automated vehicles are being developed, the human in the loop is required to understand whether drivers feel comfortable and safe in mixed traffic conditions. In recent years, dynamic driving simulators have been developed to test vehicles in complex or dangerous situations in safe and controlled environments. However, driving simulators are mostly devoted to the study of vehicle dynamics more than traffic situations.This paper presents an integration of SUMO with a high end dynamic driving simulator with the aim to study human reactions while negotiating a roundabout in mixed traffic conditions. SUMO is in charge of traffic simulation, while a full vehicle model is employed for the simulation of the dynamic of the human driven car. To allow a human to effectively drive the car, both simulation environments have to run in real time while exchanging the required information. Also, scenario graphics, sound and driving simulator feedback motion have to be accurately realized and synchronized with the simulations. A real-time server is employed for the synchronization of the different environments. As SUMO does not consider vehicle dynamics, particular attention is devoted to the a realistic reconstruction of trajectories and vehicle dynamics to be represented in the scenario. Some preliminary tests are shown where a panel of testers has been asked to negotiate the roundabout with different percentages of automated vehicles. The results of the tests show that drivers were able to perceive differences in the behavior of other vehicles and that the proposed approach is effective for understanding the feeling of comfort and safety of the human driver.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115987573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giuliano Cornacchia, M. Nanni, D. Pedreschi, L. Pappalardo
Routing algorithms typically suggest the fastest path or slight variation to reach a user's desired destination. Although this suggestion at the individual level is undoubtedly advantageous for the user, from a collective point of view, the aggregation of all single suggested paths may result in an increasing impact (e.g., in terms of emissions).In this study, we use SUMO to simulate the effects of incorporating randomness into routing algorithms on emissions, their distribution, and travel time in the urban area of Milan (Italy). Our results reveal that, given the common practice of routing towards the fastest path, a certain level of randomness in routes reduces emissions and travel time. In other words, the stronger the random component in the routes, the more pronounced the benefits upon a certain threshold. Our research provides insight into the potential advantages of considering collective outcomes in routing decisions and highlights the need to explore further the relationship between route randomization and sustainability in urban transportation.
{"title":"Effects of Route Randomization on Urban Emissions","authors":"Giuliano Cornacchia, M. Nanni, D. Pedreschi, L. Pappalardo","doi":"10.52825/scp.v4i.217","DOIUrl":"https://doi.org/10.52825/scp.v4i.217","url":null,"abstract":"Routing algorithms typically suggest the fastest path or slight variation to reach a user's desired destination. Although this suggestion at the individual level is undoubtedly advantageous for the user, from a collective point of view, the aggregation of all single suggested paths may result in an increasing impact (e.g., in terms of emissions).In this study, we use SUMO to simulate the effects of incorporating randomness into routing algorithms on emissions, their distribution, and travel time in the urban area of Milan (Italy). Our results reveal that, given the common practice of routing towards the fastest path, a certain level of randomness in routes reduces emissions and travel time. In other words, the stronger the random component in the routes, the more pronounced the benefits upon a certain threshold. Our research provides insight into the potential advantages of considering collective outcomes in routing decisions and highlights the need to explore further the relationship between route randomization and sustainability in urban transportation.","PeriodicalId":439794,"journal":{"name":"SUMO Conference Proceedings","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128664667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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