scholarly journals A novel approach to model traffic on road segments of large-scale urban road networks

MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 1147-1163 ◽  
Author(s):  
Amila Jayasinghe ◽  
Kazushi Sano ◽  
C. Chethika Abenayake ◽  
P.K.S. Mahanama
Keyword(s):  
Large Scale ◽  
Road Networks ◽  
Urban Road ◽  
Novel Approach ◽  
Road Segments

PART 1: Finance: Transport Strategy for Polish Cities

1997 ◽  
Vol 1576 (1) ◽  
pp. 1-9
Author(s):  
Slobodan Mitric
Keyword(s):  
Mass Transport ◽  
Low Income ◽  
Local Governments ◽  
Large Scale ◽  
Road Networks ◽  
Cost Recovery ◽  
Urban Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Urban Road

A recent study requested by a group of mayors representing the largest Polish cities is summarized. The study was to be used as input into local and national debates about future directions of urban transport development in the country. The wider context is that of a major political and economic reform, begun in the late 1980s, involving no less than a rapidpaced transition from socialism to capitalism, featuring large-scale downsizing of the public sector, privatization, and a redistribution of political and resource powers from the state to local governments. Among the downstream effects of these changes has been an increase in private car ownership and use and a reduction in the market share of urban mass transit modes from between 80 and 90 percent of nonwalk daily trips to 70 percent or less. For transit operators, now owned by local governments, this has meant an added financial pressure coming after a decade of underinvestment in infrastructure, rolling stock, and other equipment. Large numbers of unemployed, retired, or otherwise low-income travelers, another consequence of restructuring the economy, have made it difficult to improve cost recovery by increasing fares. Traffic growth has generated congestion, since the structure and size of urban road networks were predicated on low car use. An urban transport strategy is proposed to respond to these problems. Its main short-term objective is to have an affordable and socially and environmentally acceptable modal split. In the longer term, the objective is to use the demand response to a much-reformed price system as the principal guide to how infrastructure and services should evolve. The key features of the strategy are as follows: ( a) evolution toward market-supplied services by a mixed-ownership mass transport industry; ( b) treatment of urban road networks as public utilities, focusing on cost recovery through pricing; ( c) linkage of pricing policies for mass transport and individual transport modes, in line with second-best thinking, aiming to reduce and even eliminate subsidies for both modes; and ( d) reliance on internally generated revenue leveraged by long-term borrowing to finance sectoral investments. It is therefore a counterpoint to a strategy wherein mass transport is a state-owned monopoly, the use of urban roads is subsidized as is mass transport, infrastructure investment is the instrument of preference as opposed to pricing, and sectoral investments and operating subsidies are financed from tax-generated budgets.

Image Generation-based Traffic Density Classification for Large-Scale Urban Road Networks

2020 ◽  
Vol 26 (11) ◽  
pp. 499-506
Author(s):  
Jiho Cho ◽  
Heejin Jung ◽  
Khac-Hoai Nam Bui ◽  
Hongsuk Yi
Keyword(s):  
Large Scale ◽  
Road Networks ◽  
Traffic Density ◽  
Image Generation ◽  
Urban Road

Traffic State Estimation for Urban Road Networks Using a Link Queue Model

2017 ◽  
Vol 2623 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Yiming Gu ◽  
Zhen (Sean) Qian ◽  
Guohui Zhang
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Large Scale ◽  
Road Networks ◽  
Entry And Exit ◽  
Urban Road ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Queue Model ◽  
Flow Propagation

Traffic state estimation (TSE) is used for real-time estimation of the traffic characteristics (such as flow rate, flow speed, and flow density) of each link in a transportation network, provided with sparse observations. The complex urban road dynamics and flow entry and exit on urban roads challenge the application of TSE on large-scale urban road networks. Because of increasingly available data from various sources, such as cell phones, GPS, probe vehicles, and inductive loops, a theoretical framework is needed to fuse all data to best estimate traffic states in large-scale urban networks. In this context, a Bayesian probabilistic model to estimate traffic states is proposed, along with an expectation–maximization extended Kalman filter (EM-EKF) algorithm. The model incorporates a mesoscopic traffic flow propagation model (the link queue model) that can be computationally efficient for large-scale networks. The Bayesian framework can seamlessly integrate multiple data sources for best inferring flow propagation and flow entry and exit along roads. A synthetic test bed was created. The experiments show that the EM-EKF algorithm can promptly estimate traffic states. Another advantage is that the EM-EKF can update its model parameters in real time to adapt to unknown traffic incidents, such as lane closures. Finally, the proposed methodology was applied to estimating travel speed for an urban network in the Washington, D.C., area and resulted in satisfactory estimation results with an 8.5% error rate.

Sign in / Sign up

Export Citation Format

Share Document