There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO₂are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs.

To estimate traffic facility-oriented particulate matter (PM) emissions, emission factors are both necessary and critical for traffic planners and the community of traffic professionals. This study used locally calibrated laser-scattering sensors to collect PM emission concentrations in a tunnel. Emission factors of both light-duty and heavy-duty vehicles were found to be higher in autumn compared to summer. Based on this study’s data analysis, PM emissions, in terms of mass, have a strong seasonal effect. The study also conducted a PM composition test on normal days and during haze events. Preliminary results suggested that the transformation of gaseous tailpipe emissions to PM is significant within the tunnel during a haze event. This study, therefore, recommends locally calibrated portable devices to monitor mobile-source traffic emissions. The study suggests that emission factor estimation of traffic modeling packages should consider the dynamic PM formation mechanism. The study also presents traffic policy implications regarding PM emission control.

As indispensable components of intelligent transportation systems, traffic detection and surveillance technologies deliver speed monitoring, traffic counting, and vehicle identification and classification. This paper proposes a normal distribution transform (NDT) algorithm to improve the speed accuracy and robustness of a laser-based detector. This method can deliver more accurate estimation of vehicle speed, enabling computation of the parameters of length and height. The results of simulation with different detector update rates suggest that the average estimation errors of vehicle parameters can be reduced using the NDT matching method, especially for the low detector update rate. The study also implemented a series of field experiments using the proposed detector prototype to verify the detector's measurements of vehicle parameters. The proposed method is a promising way in which to improve the laser-based traffic detector. In simulation test, initial experiments show that the accuracy of speed estimation can reach 95%, given the update rate of 1000 Hz for detector, the average length error can be reduced by approximately 60%. Even for speeding vehicles traveling at 150 km/h, the estimated speed error is limited to 10 km/h. In field test, for a vehicle at the speed of 80km/h, the estimation errors are within the threshold of the maximum errors of simulation, that is, 32 cm for length error and 5.71 km/h for speed error results.