A vehicle traveling in an urban network exhibits a fluctuating speed profile, affected by signals, congestion and other factors Therefore, it is very difficult to determine the volume, speed for multiple intersections without extensive instrumentation Moreover, urban networks do not show relatively homogeneous sections like freeways where delay calculations can be more complex due to local bottlenecks and multiple queueing patterns Along the same lines, isolating the impacts of various factors also become problematic In this paper, an ad-hoc delay calculation approach is employed using the green and yellow taxi GPS dataset (augmented with weather information) in New York City (NYC), which has the aforementioned dense urban street network with controlled intersections –signalized or stop-signs Using this approach, delay patterns associated with both NYC green and yellow taxi trips are investigated using their fare structure rather than conventional data sets such as volume, speed and signal timings Results indicate that the intersection delay patterns differ spatially between different boroughs of NYC Major delays in the lower Manhattan during the day especially seem to be critical since these delays not only affect the overall traffic flow but also have explicit adverse effects such as elevated air pollution and noise, which is a critical problem in NYC

Breakdown of weather, intersection and recurrent congestion impacts on urban delay in New York City

A congestion Charging System (CCS) is a well-known Travel Demand Management (TDM) "push measure" that aims to discourage the use of private vehicles with low passenger capacity. Under this scheme, most cars pay fees while driving through congestion-charged zones (CCZ). It is a controversial traffic strategy quickly gaining popularity as traffic congestion worsens in many cities worldwide. By influencing road users' decisions on when, where, how, and whether or not they need to travel, the implementation of a congestion charging system significantly reduces traffic congestion. Available data shows that the lack of real-time traffic data hampers the congestion charging system's ability to operate effectively. This leads road users to take their usual routes that are already congested, raising the costs of the congestion charging fee. On that note, road users are adamant about implementing the policy due to the additional cost imposed. Contrary to the public sentiment, the application of a Congestion Charging System on highly congested roads lessens the financial burden on road users by lowering fuel consumption, saving valuable time that can be converted into currency, and the direct and indirect cost of traffic accidents that increases its possibility of occurrence as congestion worsens. Hence, there is far less traffic on the roads, which is advantageous for the environment and people's health. This paper highlights the contributing factors of congestion, different traffic strategies implemented to counter congestion, the impact of congestion charging on the traffic flow, and commuters' perspectives on public transportation. 

Congestion Charging System on Traffic Flow of Public Transportation: A Review

With the ever-increasing travel demand and limited capacity, traffic congestion has become part of daily life. The Texas Transportation Institute (TTI) 2007 mobility report shows traffic congestion is growing worse in American cities of all sizes, creating $78 billion in user costs annually due to lost time. Among all types of roadway facilities, the arterial traffic delay is the most visible because 40% of the nation's vehicle miles traveled are along arterials. However, the progress in providing reliable, real-time traveler information on arterial streets is slow, which is largely due to complicated nature of arterial traffic. This study shows a “proof of concept” that using global positioning system (GPS) based probe vehicle data is useful for identifying performances such as link delay, number of stops, and corridor travel time for arterials. Using the proposed algorithm, a change or need in performance can easily be determined when signal coordination or similar projects are conducted.

Real-Time Arterial Traffic Performance Measures Using GPS-Instrumented Vehicles

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Real-Time Arterial Traffic Performance Measures Using GPS-Instrumented Vehicles