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Microscopic vehicle emissions models have been well developed in the past decades. Those models require secondby-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most largescaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-bysecond vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data. Index Terms-Modal activity, vehicle trajectory reconstruction, vehicle energy/emissions estimation, maximum likelihood estimation.Manuscript Riverside. His research interests include connected vehicles, eco-approach and departure, sensor-aided modeling, signal control, and traffic operations. He is a member of the IEEE Intelligent Transportation System Society, the Institute for Operations Research and the Management Sciences, and Chinese Overseas Transportation Association.Xiaohong Chen received the D.Eng. degree from the School of Traffic and Transportation Engineering, Tongji University, in 2003. She has published over 20 papers in peer-reviewed journals. She has made a long-term commitment to the basic and frontier research work of regional comprehensive transportation system planning, road network planning, public transit system planning, and pedestrian and bicycle transport systems. Her research includes transportation planning systems and methodology for metropolitan and rapid urbanization areas, transportation environment, basic theory and planning methods for pedestrian and bicycle traffic flow, and new energy transportation system planning.

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Several previous studies have used the Cellular Automaton (CA) for the modeling of bicycle traffic flow. However, previous CA models have several limitations, resulting in differences between the simulated and the observed traffic flow features. The primary objective of this study is to propose a modified CA model for simulating the characteristics of mixed bicycle traffic flow. Field data were collected on physically separated bicycle path in Shanghai, China, and were used to calibrate the CA model using the genetic algorithm. Traffic flow features between simulations of several CA models and field observations were compared. The results showed that our modified CA model produced more accurate simulation for the fundamental diagram and the passing events in mixed bicycle traffic flow. Based on our model, the bicycle traffic flow features, including the fundamental diagram, the number of passing events, and the number of lane changes, were analyzed. We also analyzed the traffic flow features with different traffic densities, traffic components on different travel lanes. Results of the study can provide important information for understanding and simulating the operations of mixed bicycle traffic flow. Hindawi Publishing Corporation

Bus emissions have become one of the important contributing factors in urban environmental pollution due to the frequent use of heavy-duty diesel engines in the day-time. Local bus driving cycles have a significant influence on bus emissions under the different traffic conditions. This study investigated the operation mode distributions and emission characteristics for urban buses based on localized MOtor Vehicle Emission Simulator (MOVES) using sparse Global Position System (GPS) data in Shanghai, China. Sparse GPS data from forty-three buses were prepared, and then bus trajectories were reconstructed to calculate local bus driving cycles, including model description, model calibration, and trajectory reconstruction. MOVES localization was conducted for emission estimation mainly focusing on the bus emission inventory comparison between US and China. Bus emission factors were estimated based on the localized MOVES from the aspect of different driving conditions. Results show that with the increase in average traveling speed, the proportion of idling operation mode showed a decreasing trend. Four typical vehicle operation mode distributions were identified with different average speeds to show the impact of traffic conditions. Bus emission factors first rapidly decreased and then slowly declined towards some minimum values. Bus lanes exhibited emission reduction benefits under serious traffic congestion. The findings of this study have great importance for transportation operation management and policy-making to reduce bus emissions, as well as improving air quality.