Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.

The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.

Urban traffic congestion and crashes have been considered by city planners as critical challenges to the economic development of the city. Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proved as one of the most cost-effective means of reducing traffic congestion. In this regard, Metropolitan Planning Organizations (MPO) or Transportation Management Centers (TMC) have included signal timing coordination in their strategic plans. Nevertheless, concerns on the safety effects of traffic signal coordination have been continuously raised by both transportation agencies and the public. This is mainly because signal coordination may increase the travel speed along an arterial, which increases the risk and severity of traffic collisions. To date, there is neither solid evidence from the field to support the concern, nor theoretical-level models to analyze this issue. This research aims to investigate the effects of traffic signal coordination on the safety performance of urban arterials through microsimulation modeling of two traffic operational conditions: free signal operation and coordinated signals, respectively. Three urban arterials in Reno, Nevada were selected as the simulation testbed and were coded in the PTV VISSIM software. The simulated trajectory data were analyzed by the Surrogate Safety Assessment Model (SSAM) to estimate the number of traffic conflicts. Sensitivity analyses were conducted for various traffic demand levels. Results show that under unsaturated conditions, traffic signal coordination could reduce the number of conflicts in comparison with the free signal operation condition. However, under oversaturated conditions, no significant difference was found between coordinated and free signal operations. Findings from this research indicate that traffic signal coordination has the potential to reduce the risk of crashes on urban arterials under unsaturated conditions.

Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between ...

At a metered on-ramp, a vehicle queue will be formed in front of the ramp meter when on-ramp demand exceeds the ramp-metering capacity. It is therefore important to provide adequate queue storage space to prevent ramp queue from spilling to the upstream feeding facility, particularly when a metered on-ramp is retrofitted to an existing unmetered ramp. This paper investigates the impact of on-ramp traffic flow arrival profile on queue length at four metered on-ramp types. Based on field investigations at 13 ramp-metering locations, it was found that a higher demand-to-capacity ratio might not automatically lead to more queue storage needs; this nonlinear relationship indicated that queue length tends to be affected by the stochastic nature of on-ramp traffic flow. An analytical approach was then employed to model the on-ramp traffic flow arrival profile at each metered on-ramp type; results revealed that the on-ramp traffic flow arrival profile affected the queuing process at metered on-ramps. Finally, the microsimulation method was used to simulate queue lengths under various demand-to-capacity ratio scenarios at each metered on-ramp type. It was found that the queue generated by the random on-ramp traffic flow arrival profile at Type 4 metered on-ramps was up to 30% shorter than the queue generated by the nonrandom on-ramp traffic flow arrival profiles at Types 1, 2, and 3 metered on-ramps. The platoon dispersion effect at Type 3 metered on-ramps reduced queue length by up to 19.6% in comparison with Type 1 metered on-ramps; simulated queue length at Type 2 metered on-ramps was found to be 5.3% shorter than at Type 1 metered on-ramps.

The operational performance of standard Single Point Urban Interchange (SPUI) and Tight Diamond Interchange (TDI) has already been widely studied. In general, SPUI is more efficient than a TDI in terms of increased capacity and decreased traffic delays even though building a SPUI generally incurs a higher cost. However, due to right-of-way constraints, a standard SPUI may not be implementable at locations with restricted land use; thus, the variations of SPUI are usually considered. Currently, there is no established methodology or guideline available on the performance evaluation of variations of SPUI. This paper aims to investigate the operational efficiency of SPUI with frontage roads (SPUI-F, a variation of SPUI). Based on a field case, the performance of SPUI-F was investigated using microsimulation. An analytical model for capacity estimation, which considered early return and discharge flow rate, was also established and validated based on microsimulation. Multiple traffic scenarios were analyzed and their performance measures were compared against the equivalent TDI design. Simulation results revealed that TDI outperformed SPUI-F in terms of average delay, speed, and queue length, and the proposed analytical model can be used for reliable capacity and delay estimation. The findings from this study can aid the decision-makers choosing an appropriate interchange type for achieving the best benefit-cost ratio.