Soyoung Ahn

VERIFICATION OF A SIMPLIFIED CAR-FOLLOWING THEORY

A simple car-following rule proposed by G.F. Newell was verified by measuring vehicles discharging from long queues at signalized intersections. Observations indicated that the time-space trajectory of a jth vehicle discharging on a homogeneous intersection approach was essentially the same as the j-1th vehicle except for a translation in time and space. These fixed translations are merely the time and distance required for driver j to reach the spacings she chooses for following vehicle j-1 as a function of j-1s velocities. This description is far simpler, and uses fewer parameters, than other car-following models.

Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion

Abstract: Passing rate measurements of backward-moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane-changing and impact of lane-changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane-changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane-changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane-changing activities do not induce a long-term change in traffic states; traffic streams are perturbed temporarily by lane-changing maneuvers but return to the initial states after relaxations.

Improving the accuracy of vehicle emissions profiles for urban transportation greenhouse gas and air pollution inventories

Metropolitan greenhouse gas and air emissions inventories can better account for the variability in vehicle movement, fleet composition, and infrastructure that exists within and between regions, to develop more accurate information for environmental goals. With emerging access to high quality data, new methods are needed for informing transportation emissions assessment practitioners of the relevant vehicle and infrastructure characteristics that should be prioritized in modeling to improve the accuracy of inventories. The sensitivity of light and heavy-duty vehicle greenhouse gas (GHG) and conventional air pollutant (CAP) emissions to speed, weight, age, and roadway gradient are examined with second-by-second velocity profiles on freeway and arterial roads under free-flow and congestion scenarios. By creating upper and lower bounds for each factor, the potential variability which could exist in transportation emissions assessments is estimated. When comparing the effects of changes in these characteristics across U.S. cities against average characteristics of the U.S. fleet and infrastructure, significant variability in emissions is found to exist. GHGs from light-duty vehicles could vary by -2%-11% and CAP by -47%-228% when compared to the baseline. For heavy-duty vehicles, the variability is -21%-55% and -32%-174%, respectively. The results show that cities should more aggressively pursue the integration of emerging big data into regional transportation emissions modeling, and the integration of these data is likely to impact GHG and CAP inventories and how aggressively policies should be implemented to meet reductions. A web-tool is developed to aide cities in improving emissions uncertainty.

Effects of Merging and Diverging on Freeway Traffic Oscillations: Theory and Observation

Continuum theory is used to explain why stop-and-go oscillations in congested freeway traffic change their amplitudes when they encounter the vehicular merging and diverging maneuvers that take place near ramps. The theory describes how oscillations diminish in amplitude when they propagate past a queued (and unmetered) on-ramp and how they grow when they propagate past an off-ramp. The premise is that merging (diverging) flows change in response to freeway oscillations and that these changes in flow dampen (amplify) oscillations. The theorys descriptions are simple and rational; all its inputs and outputs are directly observable; and its predictions are shown to match real data. The theory is tested against real data collected over multiple days from congested merge and diverge sites with videos and inductive loop detectors. For merges, predictions are found to agree with observation to within 10%, and for diverges, to within 12%. The paper thus resolves in a simple way a puzzling traffic feature reported in previous studies.

Evaluating Benefits of Systemwide Adaptive Ramp-Metering Strategy in Portland, Oregon

A systemwide adaptive ramp-metering (SWARM) system is being implemented in the Portland, Oregon, metropolitan area, replacing the previous pretimed ramp-metering system. SWARM has been deployed on six major corridors and operates during the morning and afternoon peak hours. This study entails a before and after evaluation of the benefit of the new SWARM system as compared with the pretimed system using the existing data, surveillance, and communications infrastructure. In particular, the objective of this study is to quantify the systemwide benefits in relation to savings in delay, emissions and fuel consumption, and safety improvements on and off the freeway due to the implementation of the SWARM system. A pilot study was conducted for 2 weeks on a 7-mi freeway corridor in an attempt to develop a strategic design for the future regional-level study. This paper discusses the selection process of the study corridor, the experimental design, and the results obtained from the pilot study.

Evolution of Oscillations in Congested Traffic: Improved Estimation Method and Additional Empirical Evidence

This paper provides additional empirical evidence confirming a recently proposed theory on the evolution of oscillations in congested traffic. It also proposes an improved method for computing the variation in oscillation amplitude, consisting in evaluating the oscillation amplitude along characteristic lines that travel at a constant wave speed. It is also shown that the theory is robust in that approximate input parameters can be used with little loss in accuracy. The paper, in addition, provides a finding on the evolution of oscillations in freeway segments with no entrances or exits. Although previous studies found an increase in oscillation amplitude in such segments, data in this study indicate that this is not the case in general. This finding can have important implications for understanding driver behavior in homogeneous freeway segments.

Dynamic highway congestion detection and prediction based on shock waves

Existing highway traffic monitoring system requires to deploy a large number of sensors and video cameras to detect traffic congestions, which is costly and prone to errors and failures [1]. In this paper, we present a distributed traffic detection and prediction solution by using shock wave traffic model. We develop a Hello protocol to maintain the vehicle sequence on the same lane. Based on the measurements of velocity and distance between immediate leading and following vehicles, a vehicle can detect and compute shock wave velocity incurred by vehicle merges or obstacles on the highway. When velocity changes occur continuously, congestions will be formed, which can be detected and predicted by the vehicles through a shock wave detection procedure. Our solution is effective since we only require vehicles to communicate with its neighboring vehicles within its wireless communication range.

Identifying Large Truck Hot Spots Using Crash Counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel.

Comparisons of Speed-Spacing Relations Under General Car Following Versus Lane Changing

Data from two freeway locations show that absent lane changing, the relationships between congested speed and spacing are linear in most ranges of congested speed. The relationships are also found to be statistically indifferent across lanes, indicating that a single general model would suffice for all lanes. Moreover, speed-spacing relationships for different types of lane changers (discretionary versus mandatory due to merging and diverging) are also found to be statistically indifferent. Anticipation and relaxation periods, defined as durations of perturbed car-following behavior before and after a lane-change maneuver, were measured for lane changers and the vehicles immediately behind the followers. Lane changers exhibited anticipation and relaxation periods of 8 and 14 s, respectively. The followers in the initial lanes exhibited anticipation and relaxation periods of 4 and 8 s, which are smaller than the periods for the followers in the target lanes (12 and 16 s) as well as the periods for the lane changers. The findings suggest that car-following behavior on a freeway can be predicted according to Newells simplified car-following theory, and a single model can be used without much loss of accuracy for different lanes. The findings also suggest that the anticipation and relaxation processes are independent of lane-changing types but are different for lane changers and followers.

Analysis of driver response and traffic evolution under variable speed limit control

Field test results of a variable speed limit (VSL) control algorithm, a speed-controlling algorithm using shock wave theory (SPECIALIST), were analyzed to elucidate driver response and traffic flow evolution under VSL control. Successful VSL control was characterized by nearly constant, or decreasing, demand over time. In contrast, failed VSL control was attributed to (a) significant increase in demand (during control) and (b) significant net inflow from ramps. The demand increase was found to be the leading cause of the failed control, underscoring that the efficacy of the VSL control greatly relies on its ability to incorporate demand patterns during control. On the basis of these findings, some potential improvements are offered, including a parameter design strategy that incorporates demand patterns.