12 minute read
Jake Twarog '24
Machine Learning in Freeway Ramp Metering
BY JAKE TWAROG '24
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Cover Image: A ramp meter on the Sylvan westbound entrance to I-26 in Portland, Oregon. ere is another meter not pictured on the other side. "STOP HERE ON RED" is illuminated when the ramp meter is active. Image Source: Wikimedia Commons Introduction
Despite their purpose as a means for mass transportation, freeways are known for the antithesis to that goal: tra c. ey are susceptible to interruption and delay, which has a cascading e ect and can keep tra c sticking around long a er its initial source has been resolved. Tra c is di cult to predict, even with new, modernized equipment for measuring network ow, such as dedicated probe vehicles and smartphones (Zhang, 2015). In addition, a er tra c becomes dense, it is hard to return ow to stable levels.
Tra c generated by highway entrances, called "on-ramps," can be especially problematic at peak hours. On-ramps, particularly when their access is controlled by a tra c light, send large groups of cars known as "platoons" onto the highway. ese platoons interfere with the ongoing ow of the freeway, causing signi cant jams. One technique that has been used to combat this is called ramp metering. Ramp metering was introduced in the United States in 1963 on I-290 in Chicago and has spread outward since then to other major urban areas (Yang, 2019). Despite being similar in appearance to tra c lights, ramp meters functionally act quite di erently; they o en lack yellow lights, and when there is more than one lane, each lane gets their own light. Although o en frustrating for drivers due to the addition of an additional light to a freeway commute, ramp metering is proven to help reduce on-ramp tra c by breaking up platoons. In the presence of either a reduction in ow or a bottleneck, reducing the number of vehicles that can access the freeway at once signi cantly helps reduce their impacts (Haboian, 1995). is paper will discuss the algorithmic implementation of ramp metering and how this can improve its implementation and use.
Fixed Parameter Algorithms
To maximize the e ciency of on-ramp ow, the use of ramp meters must be tightly optimized to avoid delays when tra c on the freeway is light. Many di erent types of algorithms have been used to determine both when a ramp meter's active periods should be and what their light timings should be. Two currently existing strategies for ramp metering are known as the RWS strategy and the ALINEA strategy. e RWS strategy is a simple strategy that integrates the number of vehicles that can enter the freeway as a function of k, i.e. r(k). Metering systems
collect data about tra c ow through induction loops integrated into the freeway and on-ramps themselves, which each of the strategies can use. If the critical capacity has not been exceeded yet, then allowed ow is the di erence between the last measured upstream freeway ow and the downstream critical capacity. A certain minimum ramp ow is speci ed so that the on-ramp is never completely halted. e downside to this approach is that minor disturbances in tra c, which o en are hard to correct for, can heavily skew the data. For this reason, the smoothed version of the upstream ow is used instead of the raw values (Knoop, 2018).
e ALINEA strategy focuses more on the downstream conditions than upstream (Knoop, 2018). It attempts to maintain a speci c tra c ow at a certain value, ô. Variations of this algorithm incorporate tra c density or other factors to avoid downstream bottlenecks (Knoop, 2018). ALINEA is one of the more e ective methods at preventing freeway slowdowns but can run into di culties keeping queues short on on-ramps (Ghanbartehrani, 2020).
Ultimately, both of these on their own fall somewhat short due to their use of xed parameters, which need to be set at a speci c, distinct value for each on-ramp. It is di cult and intensive for engineers to calculate what would be most optimal for each ramp, costing lots of money and time. Fixed parameters also do not correct for all edge cases. For these reasons, adaptive methods prove to be more e ective in many scenarios. To maximize the e ciency of on-ramp ow, the use of ramp meters must be tightly optimized to avoid delays when tra c on the freeway is light. Many di erent types of algorithms have been used to determine both when a ramp meter's active periods should be and what their light timings should be. Two currently existing strategies for ramp metering are known as the RWS strategy and the ALINEA strategy. e RWS strategy is a simple strategy that integrates the number of vehicles that can enter the freeway as a function of k, i.e. r(k). Metering systems collect data about tra c ow through induction loops integrated into the freeway and on-ramps themselves, which each of the strategies can use. If the critical capacity has not been exceeded yet, then allowed ow is the di erence between the last measured upstream freeway ow and the downstream critical capacity. A certain minimum ramp ow is speci ed so that the on-ramp is never completely halted. e downside to this approach is that minor disturbances in tra c, which o en are hard to correct for, can heavily skew the data. For this reason, the smoothed version of the upstream ow is used instead of the raw values (Knoop, 2018).
e ALINEA strategy focuses more on the downstream conditions than upstream (Knoop, 2018). It attempts to maintain a speci c tra c ow at a certain value, ô. Variations of this algorithm incorporate tra c density or other factors to avoid downstream bottlenecks (Knoop, 2018). ALINEA is one of the more e ective methods at preventing freeway slowdowns but can run into di culties keeping queues short on
Image 1: Ramp metering is used internationally in addition to in the U.S. Here, an on-ramp in Auckland, New Zealand sports a two-lane ramp metering system. Image Source: Wikimedia Commons
Ultimately, both of these on their own fall somewhat short due to their use of xed parameters, which need to be set at a speci c, distinct value for each on-ramp. It is di cult and intensive for engineers to calculate what would be most optimal for each ramp, costing lots of money and time. Fixed parameters also do not correct for all edge cases. For these reasons, adaptive methods prove to be more e ective in many scenarios.
Machine Learning
An intelligent algorithm could prove to be a good solution for the drawbacks of xed parameters. In addition, it would be far more cost-e ective and applicable to a wide range of scenarios, without having to be nely tuned for the dramatically di erent conditions on various freeways. A team of researchers headed by Saeed Ghanbartehrani and Anahita Sanandaji of Ohio University proposed the use of real historical data to train an algorithm which factors in unpredictable situations to dictate the timings for ramp meters. eir methodology focused on using four main machine learning modules: data re nement and selection; creating a regression; clustering; and creating a ramp metering algorithm (Ghanbartehrani, 2020).
One section of the I-205 freeway, an auxiliary Interstate in Oregon, was used to train the algorithm. ey gathered data throughout the week for the number of vehicles entering the ramp at ve-minute intervals, which formed a pattern with two peaks on weekdays (morning and evening rush hours) and with one peak in the a ernoon on weekends. In the regression stage, the team created a model to predict the volume of tra c, Vol(t*), from Time(t), Occupancy(t), Speed(t), and Vol(t). is model was consistently able to mirror the actual data with considerable accuracy, making it a good choice for the algorithm (Ghanbartehrani, 2020).
For the clustering step, two k-means clustering approaches were used. e rst was clustering based on time and Δvol/Δt to identify the tra c phases. is helped the algorithm compare the data it detected with a baseline to adjust for anomalies, like accidents. e other cluster was for tra c type and the rate which the tra c was expected to change, so it was only based on the change in volume with respect to time. is allowed the algorithm to select the correct values and model for the speci c state (Ghanbartehrani, 2020). Comparing the results with a standard ALINEA scenario found that ALINEA generated 8% more red lights than the proposed algorithm (Ghanbartehrani, 2020). e proposed algorithm also reduced maximum ramp queue length signi cantly, although the average length was similar, and each had comparable overall ow. ese results were promising, as it showed that even relatively unsophisticated machine learning techniques can compete with ALINEA. e algorithm can be deployed far more inexpensively, as it only requires a few weeks of tra c data. Having a short data collection period is a large advantage for constructing ramp meters. Xiabo Ma et al. (2020) studied the challenges that transportation departments face in collecting data under standard conditions and argued that up to six to eight weeks of data is o en needed. is is o en infeasible for xed ramp metering techniques due to budget or time constraints, which forces engineers to rely on less accurate data, decreasing ramp metering e ciency (Ma et al., 2020).
Other Adaptive Algorithms
Another adaptive technique was studied by Kwangho Kim and Michael J. Cassidy of UC Berkeley. ey proposed a ramp metering strategy based on kinematic wave theory by modeling tra c as a wave. ey asserted that there were four main e ects which happen in sequence that cause slowdowns when platoons of vehicles enter the freeway, which can be used to minimize overall tra c. e rst of these is the "pinch e ect," which is when merging and diverging maneuvers near a ramp form a jam. is jam then propagates upstream like a wave, which can cause another, even more restrictive jam if it encounters an upstream interchange. is triggers the "catch e ect," as the bottleneck becomes "caught" at the new location. e jam will continue to propagate upstream slowly, while the original jam lessens. is causes an expanding free ow pocket as the jams propagate outward. ere are positive consequences to this pocket, dubbed the "driver memory" e ect and the "pumping e ect," allowing for higher ramp in ow within this pocket as drivers adopt shorter headways due to its formation (Kim & Cassidy, 2012).
To utilize these positive e ects, the researchers tested an unconventional metering logic that intentionally allowed the tra c to slow substantially, but then maximized the recovery period created by the driver memory and pumping e ects to allow the pocket to exist for as long as possible. e results of this study showed a 3%
gain over other alternatives in long-run discharge ow, which saved up to 300 vehicle-hours of travel for commuters in just the environment of the study. It also reduced on-ramp queues. e one downside over other adaptive algorithms is that it could inhibit access to o -ramps upstream due to intentionally reducing upstream ow for a duration of time (Kim & Cassidy, 2012).
Conclusion
Despite the positive e ects introduced by the new techniques, a consistent factor limiting the usage of ramp metering e ectiveness is queue length. Geometric restrictions brought upon by geography and existing infrastructure present signi cant challenges. is is primarily because ramp meters are o en installed on existing ramps, which were rarely designed for them. Because these ramps are frequently not long enough, designers need to either sacri ce queue space or post-meter acceleration distance (Yang, 2019). However, there are ways to optimize for this, such as introducing an area-wide system instead of having isolated ramp meters (Perrine, 2015).
In addition, freeways have signi cant adverse e ects on nearby communities. Freeway development has historically displaced minorities and underrepresented groups, tearing through parts of cities that once housed marginalized communities. Living in proximity to urban freeways has been linked to reduced socioeconomic status and even adverse birth e ects (Genereux, 2008). Ramp metering, being a primary strategy used to increase the number and density of cars on freeways, exacerbates their environmental and societal impacts. While increasing tra c ow is good for commuters that use a car, reducing the number of cars on the road through improved public transportation is even more e ective, which itself requires disincentivizing car use (Wiersma, 2017).
Overall, integrating modernized techniques into ramp metering such as machine learning can increase the e ectiveness of an already useful strategy. While not without drawbacks, mitigating tra c on the freeways can help make them more e cient.
References
Genereux, M., Auger, N., Goneau, M., & Daniel, M. (2008). Neighbourhood socioeconomic status, maternal education and adverse birth outcomes among mothers living near highways. Journal of Epidemiology & Community Health, 62(8), 695-700. https://doi.org/10.1136/ jech.2007.066167
Ghanbartehrani, S., Sanandaji, A., Mokhtari, Z., & Tajik, K. (2020). A novel ramp metering approach based on machine learning and historical data. Machine Learning and Knowledge Extraction, 2(4), 379-396. https://doi.org/10.3390/make2040021
Haboian, K. A. (1995). A Case for Freeway Mainline Metering. Transportation Research Record, (1494), 11-20. https://onlinepubs.trb. org/Onlinepubs/trr/1995/1494/1494-002.pdf
Kim, K., & Cassidy, M. J. (2012). A capacity-
Image 2: Los Angeles, Route 101. Los Angeles is known for its extensive network of freeways around and within the city. Unfortunately, the construction of freeways heavily negatively impacted minority communities in the city, much like most others in the United States.
Image Source: Wikimedia Commons
increasing mechanism in freeway tra c. Transportation Research Part B: Methodological, 46(9), 1260-1272. https://doi.org/10.1016/j. trb.2012.06.002
Knoop, V. L. (2018). Ramp Metering with RealTime Estimation of Parameters. In 2018 IEEE Intelligent Transportation Systems Conference: November 4-7, Maui, Hawaii (pp. 36193626). IEEE. doi:10.1016/j.trb.2012.06.002
Ma, X., Karimpour, A., & Wu, Y.-J. (2020). Statistical evaluation of data requirement for ramp metering performance assessment. Transportation Research Part A: Policy and Practice, 141, 248- 261. https:// doi.org/10.1016/j.tra.2020.09.011
Perrine, K. A., Lao, Y., Wang, J., & Wang, Y. (2015). Area-Wide ramp metering for targeted incidents: e additive increase, multiplicative decrease method. Journal of Computing in Civil Engineering, 29(2), 04014038. https:// doi.org/10.1061/(asce)cp.1943-5487.0000321
Wiersma, J., Bertolini, L., & Straatemeier, T. (2017). Adapting spatial conditions to reduce car dependency in mid-sized ‘post growth’ European city regions: e case of South Limburg, Netherlands. Transport Policy, 55, 62-69. https://doi.org/10.1016/j. tranpol.2016.12.004
Yang, G., Tian, Z., Wang, Z., Xu, H., & Yue, R. (2019). Impact of on-ramp tra c ow arrival pro le on queue length at metered onramps. Journal of Transportation Engineering, Part A: Systems, 145(2), 04018087. https://doi. org/10.1061/jtepbs.0000211
Zhang, L., & Mao, X. (2015). Vehicle density estimation of freeway tra c with unknown boundary demand–supply: An interacting multiple model approach. IET Control eory & Applications, 9(13), 1989-1995. https://doi. org/10.1049/iet-cta.2014.1251
