scholarly journals Evaluation of Bus-Bicycle and Bus/Right-Turn Traffic Delays and Conflicts

2019 ◽  
Vol 2673 (7) ◽  
pp. 443-453
Author(s):  
Katherine L. Keeling ◽  
Travis B. Glick ◽  
Miles Crumley ◽  
Miguel A. Figliozzi
Keyword(s):  
Video Data ◽  
Mixed Traffic ◽  
Volume Data ◽  
Significant Delay ◽  
Bus Stop ◽  
Traffic Delays ◽  
Bus Delay ◽  
Traffic Scenario ◽  
Traffic Corridor ◽  
Bicycle Lane

This research evaluates conflicts and delays caused by interactions among buses, bicycles, and right-turning vehicles at a mixed traffic corridor in Portland, OR. The study site has a near-side bus stop and a right curbside lane designated for buses and right-turning vehicles. Next to the bus/right-turn lane is a bicycle lane with a bicycle box ahead of the bus stop (i.e., between the intersection and the bus stop). This research examines two concerns caused by these overlapping bus, bicycle, and automobile facilities; the first is the number of bus-bicycle conflicts (as a proxy for safety) and the second is bus delay. Video data was collected and analyzed to quantify conflicts, travel time, and delay. For every bus passing through the study site, the mixed traffic scenario that the bus incurs was categorized as one of 72 different combinations of bus, bicycle, and automobile interactions. Video count data was weighted according to seasonal, weekly, and hourly bicycle volume data to estimate the number of annual bus–bicycle conflicts. A regression analysis was performed to identify potential sources of delays. The results indicate that each bicycle crossing the intersection after the bus (within 60 ft of bus) contributes to bus delay. No statistically significant delay was found from the bicycles stopped in the bicycle box, bicycles stopped behind the bicycle box, bicycles that cross the intersection before the bus, or the presence of right-turning vehicles.

Cooperative Perception using Cellular V2X in Mixed Traffic Scenario

2021 ◽  
Author(s):  
Muhammad Rony Hidayatullah ◽  
Jyh-Ching Juang ◽  
Zhao-Shun Zheng ◽  
Wei-Hsuan Chang
Keyword(s):  
Mixed Traffic ◽  
Traffic Scenario

scholarly journals Modeling Lane-Keeping Behavior of Bicyclists Using Survival Analysis Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Hongwei Guo ◽  
Wuhong Wang ◽  
Weiwei Guo ◽  
Facheng Zhao
Keyword(s):  
Accident Risk ◽  
Model Estimation ◽  
Mixed Traffic ◽  
Analysis Method ◽  
Traffic Conditions ◽  
Semiparametric Method ◽  
Lane Width ◽  
Lifetime Analysis ◽  
Lane Keeping ◽  
Bicycle Lane

Bicyclists may cross the bicycle lane and occupy the adjacent motor lanes for some reason. The mixed traffic consisting of cars and bicycles shows very complicated dynamitic patterns and higher accident risk. To investigate the reason behind such phenomenon, the lifetime analysis method is adopted to examine the observed data for the behavior that bicycles cross the bicycle lane and occupy the adjacent motor lanes. The concepts named valid volume and probability of lane-keeping behavior are introduced to evaluate the influence of various external factors such as lane width and curb parking, and a semiparametric method is used to estimate the model with censored data. Six variables are used to accommodate the effects of traffic conditions. After the model estimation, the effects of the selected variables on the lane-keeping behavior are discussed. The results are expected to give a better understanding of the bicyclist behavior.

scholarly journals Modelling bus delay at bus stop

Transport ◽  
2015 ◽  
Vol 33 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Yueying Huo ◽  
Wenquan Li ◽  
Jinhua Zhao ◽  
Shoulin Zhu
Keyword(s):  
Waiting Time ◽  
Cycle Length ◽  
Arrival Rate ◽  
Traffic Lights ◽  
Bus Stop ◽  
Transfer Block ◽  
Proposed Model ◽  
Bus Delay ◽  
Block Based ◽  
The Given

A bus may be blocked from entering and exiting a stop by other buses and traffic lights. The objective of this paper is to model each type of delay under these phenomena and the overall delay a bus experiences at a stop. Occupy-based delay, transfer block-based delay and block-based delay are defined and modelled. Bus delay at stop is just the sum of these three types of delay. Bus arrival rate, bus service rate, berth number and traffic lights are taken into consideration when modelling delay. Occupy-based delay is modelled with mean waiting time in Queueing theory. Transfer block-based delay and block-based delay are modelled based on standard deviation of waiting time and the probabilities of their occurrences. Two stops in Vancouver, Canada are selected for parameter estimation and model validation. The unknown parameter is estimated as 0.4230 using Ordinary Least Squares (OLS), which indicates that 42.3% of waiting time variation can be attributed to buses being blocked by the buses in front and red light for the selected stops. Model validation shows the average accuracy rate of the proposed model is 75.07% for the selected stops. Bus delay at stop evidently increases when arrival rate is more than 85 buses per hour for the given service time (50 s), ratio of red time to cycle length (0.65) and berth number (2). We also figure out that bus delay at stop evidently increases when service time is more than 60 s for the given arrival rate (54 buses per hour), ratio of red time to cycle length (0.65) and berth number (2). The proposed model can provide a tool for bus stop design and offer the foundation for service quality evaluation of transit.

Performance of Transit Signal Priority with Queue Jumper Lanes

2005 ◽  
Vol 1925 (1) ◽  
pp. 265-271 ◽  
Author(s):  
Guangwei Zhou ◽  
Albert Gan
Keyword(s):  
Transit Signal Priority ◽  
Preferential Treatment ◽  
Local Conditions ◽  
Bus Stop ◽  
Optimal Detector ◽  
Bus Delay ◽  
Traffic Volumes ◽  
Bus Stops ◽  
Detector Location

Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over farside bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach were found to have only a slight impact on bus delay when the volume-to-capacity (v/c) ratio was below 0.9. However, when v/c exceeded 0.9, bus delay increased quickly. Right-turn volumes were found to have an insignificant impact on average bus delay, and an optimal detector location that minimizes bus delay under local conditions was shown to exist.

Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data

2012 ◽  
Vol 13 (4) ◽  
pp. 1832-1844 ◽  
Author(s):  
Yingying Zhang ◽  
Danya Yao ◽  
T. Z. Qiu ◽  
Lihui Peng ◽  
Yi Zhang
Keyword(s):  
Pedestrian Safety ◽  
Safety Analysis ◽  
Video Data ◽  
Mixed Traffic ◽  
Traffic Conditions

Estimating the Impacts of Bus Stops and Transit Signal Priority on Intersection Operations: Queuing and Variational Theory Approach

2017 ◽  
Vol 2622 (1) ◽  
pp. 70-83 ◽  
Author(s):  
Kan Wu ◽  
S. Ilgin Guler ◽  
Vikash V. Gayah
Keyword(s):  
Queuing Theory ◽  
Theory Approach ◽  
Transit Signal Priority ◽  
Variational Theory ◽  
Traffic Conditions ◽  
Bus Stop ◽  
Car Traffic ◽  
Trade Offs ◽  
Bus Delay ◽  
Local Agencies

Transit signal priority (TSP) can be used to improve bus operations at signalized intersections, often to the detriment of general car traffic. However, the impacts of TSP treatments applied to intersections with nearby bus stop locations are currently unknown. This paper quantifies changes in intersection capacity, car delay, and bus delay when priority is provided to buses that dwell at near- or farside bus stop locations through green extension or red truncation. Variational and kinematic wave theories are used to estimate car capacity and bus delay for oversaturated traffic conditions; queuing theory is used to estimate car and bus delays for undersaturated conditions. Numerical analyses are conducted to explore the impacts on various bus stop locations and bus dwell time durations. These results illustrate clear trade-offs between reduced bus delays and increased car delays or reduced intersection capacities that can be quantified with the proposed method. The results also reveal that the effects of TSP vary dramatically with bus dwell times for a given bus stop location. The proposed method and associated results can be used to implement TSP strategies to meet the specific needs of local agencies.

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