Urban road segment level of service based on bicycle users’ perception under mixed traffic conditions

Abstract

During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets (13,624 effective ratings in total) were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a predictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from 'excellent' to 'worst' (A-F).

Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature

“…This issue is associated with the need for research assessing which assumptions can be adopted for e-bikes from those related to bicycle-pedestrians interactions. Research in respect to BLOS studies is mostly based on transport components, including segments [91,[167][168][169][170][171][172], nodes [63,[173][174][175] and networks [5,[176][177][178][179]. However, network-based BLOS studies compared to link and node studies require more extensive research [180][181][182][183][184].…”

Quality of Bicycle Traffic Management at Urban Road Links and Signalized Intersections Operating under Mixed Traffic Conditions

“…Recently, two BLOS models have been proposed for the assessment of urban road links carrying heterogeneous traffic. Of these models, one is ordered-probit-based ( 15 ) and the other one is multivariate adaptive regression splines (MARS)-based ( 16 ). Various quantitative variables such as outside lane width, traffic volume, traffic speed, pavement condition index, land-use pattern, interruptions by unauthorized stoppages of intermittent public transits, on-street parking activities, and frequency of driveways carrying a high volume of traffic were observed to have significant influences on the BLOS.…”

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

“…Beura et al [25] proposed a model which is suitable for assessing the compatibility of roadways for bicycle use under complex interaction with various types of vehicles. Kassim et al [26] examined the effects of variability in pedestrian volume on cyclist-vehicle interactions.…”

“…Some other studies evaluated the traffic operation and the level of service on the streets with bike lanes [11,12,[22][23][24]. In recent years, the interactions between bicycle and vehicle traffic flow on streets have gained much attention from transportation professionals [25][26][27][28][29]. However, those previous studies did not aim to evaluate and predict the vehicle delay caused by bicycles under different traffic situations which is still a critical issue that has not been addressed properly.…”