NeTrainSim:a network-level simulator for modeling freight train longitudinal motion and energy consumption

Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.

INTEGRATION Framework forModeling Eco-routing Strategies:Logic and Preliminary Results

The paper presents the INTEGRATION microscopic traffic assignment and simulationframework for modeling eco-routing strategies.Two eco-routing algorithms are developed:onebased on vehicle sub-populations(ECO-Subpopulation Feedback Assignment or ECO-SFA)andanother based on individual agents(ECO-Agent Feedback Assignment or ECO-AFA).Bothapproaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed.Subsequently,fuel consumption estimates are refined based on experiences of othervehicles within the same class.The proposed framework is intended to evaluate the network-wideimpacts of eco-routing strategies.This stochastic,multi-class,dynamic traffic assignmentframework was demonstrated to work for two scenarios.Savings in fuel consumption levels inthe range of 15 percent were observed and potential implementation challenges were identified.

Virginia Tech Comprehensive Power-based Fuel Consumption Model(VT-CPFM):Model Validation and Calibration Considerations

A power-based vehicle fuel consumption model,entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model(VT-CPFM)that was developed in an earlier publication is validated against in-field fuel consumption measurements.The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96.More importantly,both estimates and measurements produce very similar behavioral changes depending on engine load conditions.The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data.

Enhancing and Calibrating the Rakha-Pasumarthy-Adjerid Car-Following Model using Naturalistic Driving Data

The research presented in this paper analyzes the simplified behavioral vehicle longitudinal motion model,currently implemented in the INTEGRATION software,known as the Rakha-Pasumarthy-Adjerid(RPA)model.The model utilizes a steady-state formulation along with two constraints,namely:acceleration and collision avoidance.An analysis of the model using the naturalistic driving data identified a deficiency in the model formulation,in that it predicts more conservative driving behavior compared to naturalistic driving.Much of the error in simulated car-following behavior occurs when a car-following event is initiated at a spacing that is often much shorter than is desired.The observed behavior is that,rather than the following vehicle decelerating aggressively,the following vehicle coasts until the desired headway/spacing is achieved.Consequently,the model is enhanced to reflect this empirically observed behavior.Finally,a quantitative and qualitative evaluation of the original and proposed model formulations demonstrates that the proposed modification significantly decreases the modeling error and produces car-following behavior that is consistent with empirically observed driver behavior.

Can We Model Driver Perceptions?An In-Situ Experiment in Real-World Conditions

It is clear that perceptions play a significant role in traveler decisions.Consequently,traveler perceptions are a corner stone in the feasibility of traveler information systems;for traveler information systems are only valuable if the drivers are incapable of accurately acquiring the provided information on their own,and if the provided information is relevant for the drivers’decision criteria.Accuracy of traveler perceptions has been repeatedly researched in public transportation,and has been found to vary according to different reasons.However,in spite of the clear significance of traveler perceptions,minimal effort has been put into modeling it.Almost all travel behavior models are based on traveler experiences,which are assumed to reflect traveler perceptions via the addition of some random error component.This works introduces an alternative approach:instead of adding an error component to represent driver perceptions,it proposes to model driver perceptions.This work is based on a real-world route choice experiment of a sample of 20 drivers who made more than 2,000 real-world route choices.Each of the drivers’experiences,perceptions,and choices were recorded,analyzed and cross examined.The paper demonstrates that:i)driver experiences are different from driver perceptions,ii)driver perceptions explain driver choices better than driver experiences,iii)it is possible to model and predict driver perceptions of travel distance,time and speed.

Fuel Economy Impacts of Manual,Conventional Cruise Control,and Predictive Eco-Cruise Control Driving

The paper presents the results of a field experiment that was designed to compare manual driving,conventional cruise control(CCC)driving,and Eco-cruise control(ECC)driving with regard to fuel economy.The field experiment was conducted on five test vehicles along a section of Interstate 81 that was comprised of±4%uphill and downhill grade sections.Using an Onboard Diagnostic II reader,instantaneous fuel consumption rates and other driving parameters were collected with and without the CCC system enabled.The collected data were compared with regard to fuel economy,throttle control,and travel time.The results demonstrate that CCC enhances vehicle fuel economy by 3.3 percent on average relative to manual driving,however this difference was not found to be statistically significant at a 5 percent significance level.The results demonstrate that CCC driving is more efficient on downhill versus uphill sections.In addition,the study demonstrates that an ECC system can produce fuel savings ranging between 8 and 16 percent with increases in travel times ranging between 3 and 6 percent.These benefits appear to be largest for heavier vehicles(SUVs).