Traffic Modeling and Safety Analysis on Motorway Ramps
Jeng, Dong Sheng
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The whole research program aimed to improve operation, sustainability and safety of ramp areas. For this purpose, I first calculated passenger car equivalents (PCE) for a specific type of heavy vehicles (HVs) on the on-ramp through various different PCE methods. Through this study, I concluded that 1) homogenization based method (HM) cannot properly predict the variation trend of PCE values over traffic volume due to the low sensitivity of the speed to the change in traffic volume; 2) both time headway based method (THM) and traffic flow based method (TFM) can derive the results which are relatively consistent with outcome from simulation model. As this study is not relevant with the overall aim of the research program, it will be presented in Appendix 1. The areas adjacent to ramps have always been regarded as traffic bottlenecks during peak hours because the frequent interactions between the merging and diverging traffic and the through traffic contribute to the loss in travel time. Currently, there is few literature to explore the impact of on-ramp lane configurations on travel time of through traffic. In this study, I comparatively analyzed the impact of freeway mainline traffic flow and proportion of mainline heavy goods vehicles (HGVs) on the average travel time of the road segment fitted with two types of on-ramp lane arrangements. The mainline traffic volumes vary from 800 to 2200 and the proportions of mainline HGVs range from 0 to 12%. The calibrated and validated simulation models were used to generate the average travel time under different traffic scenarios. Through comparative analyses, the following conclusions can be drawn. 1) For the impact of HGVs on travel time, when the mainline traffic flow is below 1200 vehs/hr/ln, the performance in travel time of the road segment fitted with zip merging outperforms that fitted with added lane; when the mainline traffic volume is 1400 vehs/hr/ln, through traffic, starting from 10% of HGVs, spends less travel time on the road segment equipped with added lane; when the mainline traffic flow reaches 2200 vehs/hr/ln, the road segment fitted with added lane roundly performs better than that fitted with zip merging in term of travel time. 2) For the impact of traffic volume on travel time, although the proportion of mainline HGVs is 0, the road segment equipped with added lane starts to outperform that equipped with zip merging when traffic flow is approximately 2200 vehs/hr/ln; when the proportion of mainline HGVs is 12%, through traffic spends less travel time on the road segment fitted with added lane once the mainline traffic flow exceeds 1200 vehs/hr/ln. The abovementioned conclusions come from a case study. Currently, I cannot ensure they apply to other closely spaced on- and off-ramp areas. Further study will be conducted in future works. As traffic operation has a close connection with environmental sustainability, the assessment of carbon dioxide emissions (CO2) was also the key point of the research program. Areas adjacent to ramps have been viewed as zones with high emissions due to more traffic stops. Currently, many researchers have deemed that proper traffic control and the improvements in geometry can reduce CO2 emissions in such areas. Few research have assessed the impact of on-ramp lane configurations on sustainability. In this study, I used the improved comprehensive modal emissions model (CMEM) to quantitatively evaluate the impact of mainline traffic volume and percentages of mainline HGVs on CO2 emitted on the road segment fitted with two on-ramp lane arrangements. Traffic volumes range from 800 to 1800 vehs/hr/ln with an increment of 200 vehs/hr/ln and the proportions of HGVs vary from 2% to 10% with an increment of 2%. The results show that 1) For the impact of HGVs on CO2 emissions, when traffic flow is 800 vehs/hr/ln, through traffic on the road segment fitted with added lane can obtain a better performance in CO2 emissions after the percentage of mainline HGVs exceeds 8%; when traffic flow varies from 1000 to 1800 vehs/hr/ln, through traffic generates less CO2 emissions on the road segment equipped with added lane under the effect of any percentage of HGVs; 2) For the impact of traffic volume on CO2 emissions, when the proportion of HGVs is 2%, less CO2 is emitted on the road segment fitted with added lane after mainline traffic flow reaches 1000 vehs/hr/ln; when the proportion of HGVs reaches 10%, the performance of added lane on CO2 emissions is completely superior to that of zip merging. In addition, two-factor-based CO2 emissions contour charts were depicted. They could assist traffic engineers in selecting an appropriate combination of traffic volume and percentage of HGVs in order to achieve control of emissions. The poor performance in traffic operation may contribute to more traffic conflicts. My research direction thus turned to safety assessment for on-ramps. Nowadays, many crash surrogate metrics have been proposed and designed for predicting the rear-end crash risks for basic freeway sections, including the time to collision (TTC), the deceleration rate to avoid crash (DRAC), the crash potential index (CPI) and the aggregated crash index (ACI). However, they might not be applicable to assess crash risks for on-ramps. As a consequence, I proposed the simplified crash surrogate metric (SCSM) to predict the rear-end crash risks for on-ramps. A one-lane on-ramp of Pacific Motorway, Australia was selected to validate the proposed surrogate metric. Another two surrogate measures (TTC and ACI) were compared with the SCSM through a simple proportional relationship between the societal risk index and crash rates. Through this study, I conclude that 1) as an upgraded version of the TTC, the SCSM not only features the same straightforward closed form as the traditional TTC, but also makes up for the shortcoming of the TTC that is unable to accurately assess crash risks in saturated traffic flow; 2) the TTC based surrogate metric performed the worst; 3) the performance of the SCSM is more or less similar to that of the ACI. But considering the ability to resolve practical engineering issues, the SCSM is superior to the ACI. Hotspots identification (HSID), a reactive crash prediction based on the historical crash counts, is crucial to transport authorities for evaluating the risk level of the object road sites. Many researchers have focused on improving the accuracy of HSID, namely to identify those un-identified hotspots that should have been treated. In practice, several conventional HSID approaches have been developed and applied for decades, but they fail to take the daily variability of traffic flow and crash record into account. To address it, four novel Empirical Bayesian (EB) based methods (for (1) morning and (2) afternoon peak hours, and (3) daytime and (4) night off-peak hours) were proposed to screen hotspots in the Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast. The detailed six-year crash records were used. I further analyzed the applicability of four proposed EB-based methods and three traditional HSID methods: (5) crash frequency method (CFM), (6) societal risk-based method (SRCM), and (7) Empirical Bayesian method (EB) in regard to freeway main carriageways, on-ramps and off-ramps through two consistency tests. Through this study, the following conclusions were drawn. 1) The EB-based methods considering the effect of daily variability outperform other approaches in the HSID for freeway main carriageways. 2) The performances in proposed methods are inferior to those in the EB and the CFM in the HSID for on- and off-ramps. 3) The conventional EB method possess the best performance in the HSID for on- and off-ramps.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
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Pacific motorway Southeast Queensland