Speed model used in roads for different types of vehicles

Education

1. Literature Review

2. Author Abstract Model Findings Mansour Johari, Shang Jiang, Mehdi Keyvan-Ekbatani , Dong Ngoduy Year: 2021 Studies NMFD in urban networks with bi-modal traffic flows.  Aimsun model used in the study for urban network partitioning. Three scenarios defined to examine mode differentiation in bi-modal networks. Mode differentiation crucial in bi-modal network for speed detection. Rajesh Gajjar and Divya Mohandas Year: 2016 Critical assessment of road capacities on urban roads in Mumbai.  Field surveys to capture traffic volume data for major roads. Comparative study of road capacities in Mumbai based on IRC 106:1990.  Analysis of observed volumes against standard capacities for urban roads. Mumbai roads exceed capacity but show no congestion issues. Technology, road conditions, and urban factors affect observed road capacities. Factor Effect Speed on Urban Roads and Speed Model

3. Cont ….. Author Abstract Model Findings Allister Loder et al. Year: 2021 Proposes methodology for multi-modal MFD estimation in urban road networks. Utilizes empirical data from Amsterdam and London for validation. Continuous multiclass fundamental diagram from Bliemer (2001)  Discrete multiclass FD adopted from Wierbos et al. Proposed vector-based approach estimates multi-modal MFDs with speed information. Allister Loder et al. Year: 2017. Introduces 3D-MFD for urban traffic performance analysis.  Empirical study in Zurich using vehicle and passenger data.  Vehicle 3D-MFD model estimated using ordinary least squares (OLS)  3D-pMFD model estimates for passenger speed and car speed 3D-MFD estimates effects of vehicles and passengers on urban speeds.  Public transport users' share impacts journey speeds in urban areas.

4. Cont ….. Author Abstract Model Findings Konstantinos Ampountolas, Nan Zheng, Nikolas Geroliminis, 2017. Integrates bi-modal MFD modeling for urban traffic control framework. Reduces congestion, improves bus performance, and avoids gridlocks. Linear Parameter Varying (LPV) model with uncertain parameters.  Bi-modal Macroscopic Fundamental Diagram (MFD) model for mixed traffic. Proposed robust control reduces congestion and improves bus performance significantly. Igor Dakic and Monica Menendez Proposes novel methods for car speed estimation in urban networks. Uses AVL data for public transport and fusion algorithm for accuracy. Novel estimation methods for car speed based on AVL and FCD data. Lagrangian methods used for empirical validation in real-life traffic scenarios. AVL-based method approximates car speed accurately at the network level. Fusing FCD and AVL data enhances multi-modal traffic performance representation.

5. Cont ….. Author Abstract Model Findings Ziyuan Pu, Chenglong Liu, Xianming Shi, Zhiyong Cui, Yinhai Wang. Road surface friction prediction model using LSTM for traffic safety.  Long-Short Term Memory (LSTM) neural network model was utilized. SVR, RF, and Feed-Forward NN were selected as baseline models. LSTM model outperformed baseline models in predictive accuracy metrics. Number of time-lags and time interval influenced predictive performance. World Congress on Engineering 2017 Proceedings of the World Congress on Engineering 2017. Describes improving Indonesian HCM formula for road performance analysis.  Utilizes VISSIM software for traffic flow simulation and calibration.  VISSIM software used for simulation and calibration.  Wiedemann parameters calibrated to match Indonesian driver behavior. Free flow speed: 37 kph for cars, 40 kph for motorcycles. Basic capacity: 1750 pcu /hour/lane.

6. Cont ….. Author Abstract Model Findings Keshuang Tang, Siqu Chen, Yumin Cao, Xiaosong Li, Di Zang , Jian Sun Proposes hybrid CNN models for short-term travel speed prediction. Three hybrid CNN models: LSTM-CNN, AE-CNN, Inception-CNN. Basic CNN model with LSTM, AutoEncoder , and Inception modules. Hybrid CNN models exceed 96% prediction accuracy with <2.5 km/h error. Author: Jin Yang Focuses on traffic flow prediction with deep neural network model . Spatiotemporal graph attention-based traffic flow prediction model.  Proposed model combines graph neural networks and attention mechanism for traffic prediction .

7. Finding from the Literature Traffic composition analysis reveals distinct regions in speed-volume relationship ( Eleni , 2016) Decreased PSL reduces mean speed, speed variance, and severe accidents ( Ary and Karl, 2015) Lane width has minimal impact on small vehicle speeds; motorcycle speeds unaffected by lane variations (Tseng et al., 2013): Vehicle class, area type, land use, road class, and number of lanes significantly influence FFS ( Balakrishnan and Sivanandan , 2017) Speed distributions of vehicles on National Highways normally distributed ( Kadiyali et al., 1981) Road roughness significantly affects free-flow speed; adjustment factors developed for two-lane roads. (Chandra, 2004 ) Free-speed profiles developed for different vehicle types; significant differences observed across road types. Speeds on eight-lane roads higher than on four- and six-lane roads; marginal speed increase for cars and two-wheelers. ( Madhu et al., 2011 )

8. Factors influencing free-flow speeds in homogeneous traffic also apply to heterogeneous traffic. Free flow speeds on urban arterials depend significantly on factors like road side friction, number of flyovers, access points, and section length. ( Rao and Rao, 2015) 85th percentile speed provides accurate estimates compared to normal approximation model. (Al- Ghamdi , 1998): Motorcycle speeds significantly impact overall traffic flow; different from passenger cars ( Perco , 2008) Adverse weather conditions reduce road capacity and free-flow speed; effective weather-responsive strategies needed. ( Asamer et al., 2010): Speed decreases with increase in traffic volume; small vehicles less affected. ( Dhamaniya and Chandra, 2013) Decreased PSL leads to reductions in mean speed and speed variance; increased PSL raises mean speed ( Silvano and Karl L. Bang, 2015). Finding from the Literature

9. Gaps Identified in the Literature i . Lack of Comprehensive Speed Models for Mixed Traffic: Existing models often designed for homogeneous traffic, not accounting for diverse urban traffic. Need for models integrating interactions among various vehicle types. ii. Insufficient Focus on Urban Road Conditions: More research needed on roadway alignment and its influence on Free Flow Speed (FFS) and Operating Speed iii. Limited Evaluation of Road Characteristics: the impact of road characteristics on free-flow speed, but more detailed analyses are needed to understand how different road design elements specifically influence speed under mixed traffic conditions.

10. iv. Need for Detailed Empirical Relations: there is still a need for more detailed empirical studies that link travel speed, traffic volume, and composition directly to predictive models. v. Gaps in Specific Urban Traffic Management Strategies: Need for targeted studies on high-volume traffic, intersections, pedestrian crossings, and varying speed limits. Comprehensive models incorporating these urban-specific challenges are lacking. vi. Limited Geographical Diversity in Studies: Research often focused on developed countries with homogeneous traffic. More localized studies needed in developing countries with diverse and complex urban traffic. Gaps Identified in the Literature

11. References Ampountolas , K., Zheng, N., and Geroliminis , N. (2017). "Macroscopic modelling and robust control of bi-modal multi-region urban road networks." *Transportation Research Part B: Methodological*, 104, 616-637. <https://doi.org/10.1016/j.trb.2017.05.007>. Irbosa , H. M. (1995). "Impacts of Traffic Calming Measures on Speeds on Urban Roads." Feng, B., Xu, J., Zhang, Y., and Lin, Y. (2021). "Multi-step traffic speed prediction based on ensemble learning on an urban road network." *Applied Sciences (Switzerland)*, 11(10). <https://doi.org/10.3390/app11104423>. Gajjar , R., and Mohandas, D. (2016). "Critical Assessment of Road Capacities on Urban Roads - A Mumbai Case-study." *Transportation Research Procedia*, 17(December 2014), 685-692. <https://doi.org/10.1016/j.trpro.2016.11.124>. Johari, M., Jiang, S., Keyvan-Ekbatani , M., and Ngoduy , D. (2023). "Mode differentiation in partitioning of mixed bi-modal urban networks." * Transportmetrica B*, 11(1), 463-485. <https://doi.org/10.1080/21680566.2022.2089271>. Loder , A., Ambühl , L., Menendez, M., and Axhausen , K. W. (2017). "Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram." *Transportation Research Part C: Emerging Technologies*, 82, 88-101. <https://doi.org/10.1016/j.trc.2017.06.009>. Martinelli , V., Ventura, R., Bonera , M., Barabino , B., and Maternini , G. (2022). "Effects of urban road environment on vehicular speed. Evidence from Brescia (Italy)." *Transportation Research Procedia*, 60(2021), 592-599. <https://doi.org/10.1016/j.trpro.2021.12.076 >.

12. References Medina-Salgado , B., Sánchez- DelaCruz , E., Pozos -Parra, P., and Sierra, J. E. (2022). "Urban traffic flow prediction techniques: A review." *Sustainable Computing: Informatics and Systems*, 35(April), 100739. <https://doi.org/10.1016/j.suscom.2022.100739>. Munawar , A., Irawan , M. Z., and Fitrada , A. G. (2017). "Development of urban road capacity and speed estimation methods in Indonesia." *Lecture Notes in Engineering and Computer Science*, 2230, 564-567. Pu, Z., Liu, C., Shi, X., Cui, Z., and Wang, Y. (2021). "Road surface friction prediction using long short-term memory neural network based on historical data." *Journal of Intelligent Transportation Systems: Technology, Planning, and Operations*, 26(1), 34-45. <https://doi.org/10.1080/15472450.2020.1780922>. Silvano , A. P., and Bang, K. L. (2016). "Impact of speed limits and road characteristics on free-flow speed in urban areas." *Journal of Transportation Engineering*, 142(2). <https://doi.org/10.1061/(ASCE)TE.1943-5436.0000800>. Tang, K., Chen, S., Cao, Y., Li, X., Zang , D., Sun, J., and Ji, Y. (2022). "Short-Term Travel Speed Prediction for Urban Expressways: Hybrid Convolutional Neural Network Models." *IEEE Transactions on Intelligent Transportation Systems*, 23(3), 1829-1840. <https://doi.org/10.1109/TITS.2020.3027628>. Vlahogianni , E. I. (2007). "Some Empirical Relations Between Travel Speed, Traffic Volume and Traffic Composition in Urban Arterials." *IATSS Research*, 31(1), 110-119. <https://doi.org/10.1016/s0386-1112(14)60189-8 >.

13. References Guo , J., Song, C., Zhang, H., and Wang, H. (2020). "Multi-step traffic speed prediction model with auxiliary features on urban road networks and its understanding." *IET Intelligent Transport Systems*, 14(14), 1997-2009. <https://doi.org/10.1049/iet-its.2020.0284>. Ibrahim, R., Zala , L. B., and Amin, A. A. (2018). "Individual Vehicle Speed Modeling on Urban Arterials in Mixed Traffic Conditions by Using Artificial Neural Network." *International Research Journal of Engineering and Technology*. <www.irjet.net>. Tseng, P.-Y., Lin, F.-B., and Chang, C.-W. (2013). "Analysis of Free-flow Speed Characteristics of Urban Arterials." *Asian Transport Studies*, 2(4). Balakrishnan , S., and Sivanandan , R. (2017). "Developing free-flow speed models for urban roads under heterogeneous traffic conditions." *International Journal for Traffic and Transport Engineering*, 7(4), 520-529. <http://dx.doi.org/10.7708/ijtte.2017.7(4).04>. Bekhor , S., Lotan , T., Gitelman , V., Morik , S. (2013) Free-Flow Travel Speed Analysis and Monitoring at the National Level Using Global Positioning System Measurements. Journal of Transportation Engineering. 139 (12). pp. 1235-1243. DOI: 10.1061/(ASCE)TE.1943-5436.0000607 Brilon , W., Lohoff , J. (2011) Speed-flow Models for Freeways. Procedia – Social and Behavioral Sciences. 16. pp. 26-36. DOI: 10.1016/j.sbspro.2011.04.426 Krammes , R. A. Design Speed and Operating Speed in Rural Highway Alignment Design. In Transportation Research Record: Journal of the Transportation Research Board, No. 1701, TRB, National Research Council, Washington, D.C., 2000, 68–75. Hassan, Y., and M. Sarhan . Transportation Research Circular E-C151: Modeling Operating Speed: Synthesis Report. Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 1–2 .

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