Multiple bus network with overlapping connectivity

1991 ◽  
Vol 138 (4) ◽  
pp. 281 ◽  
Author(s):  
B. Wilkinson
Keyword(s):  
Bus Network

THE ANALYSIS OF PASSENGER TRAFFIC ON THE ROUTES OF THE BUS OF KURSK

2020 ◽  
Vol 71 (4) ◽  
pp. 37-45
Author(s):  
B.A. SEMENIKHIN ◽  
◽  
L.P. KUZNETSOVA ◽  
YU.A. MALNEVA ◽  
A.YU. ALTUKHOV ◽  
...  
Keyword(s):  
Total Power ◽  
Passenger Traffic ◽  
Route Network ◽  
Analysis Of Change ◽  
Bus Network

Results of inspection and the analysis of passenger traffics on routes of the bus of Kursk are presented, the main shortcomings of the existing route network are revealed. The analysis of change of daily volume of transportations of passengers made on the basis of data of it and the previous inspections of passenger traffics and also distribution of total power of a passenger traffic on hours of day is provided. Results of development of rational route bus network of Kursk which is almost completely deprived of the shortcomings inherent in the existing route network are presented.

RMB-a reconfigurable multiple bus network

2002 ◽  
Author(s):  
H. ElGindy ◽  
H. Schroder ◽  
A. Spray ◽  
A.K. Somani ◽  
H. Schmeck
Keyword(s):  
Bus Network

Using GTFS to Calculate Travel Time Savings Potential of Bus Preferential Treatments

2021 ◽  
pp. 036119812110092
Author(s):  
Daniel Arias ◽  
Kara Todd ◽  
Jennifer Krieger ◽  
Spencer Maddox ◽  
Pearse Haley ◽  
...  
Keyword(s):  
Travel Time ◽  
Planning Process ◽  
Operating Cost ◽  
Flow Characteristics ◽  
Travel Time Savings ◽  
Bus Lane ◽  
Time Savings ◽  
Bus Network ◽  
Bus Networks ◽  
Transit Agencies

Dedicated bus lanes and other transit priority treatments are a cost-effective way to improve transit speed and reliability. However, creating a bus lane can be a contentious process; it requires justification to the public and frequently entails competition for federal grants. In addition, more complex bus networks are likely to have unknown locations where transit priority infrastructure would provide high value to riders. This analysis presents a methodology for estimating the value of bus preferential treatments for all segments of a given bus network. It calculates the passenger-weighted travel time savings potential for each inter-stop segment based on schedule padding. The input data, ridership data, and General Transit Feed Specification (GTFS) trip-stop data are universally accessible to transit agencies. This study examines the 2018 Metropolitan Atlanta Rapid Transit Authority (MARTA) bus network and identifies a portion of route 39 on Buford Highway as an example candidate for a bus lane corridor. The results are used to evaluate the value of time savings to passengers, operating cost savings to the agency, and other benefits that would result from implementing bus lanes on Buford Highway. This study does not extend to estimating the cost of transit priority infrastructure or recommending locations based on traffic flow characteristics. However, it does provide a reproducible methodology to estimate the value of transit priority treatments, and it identifies locations with high value, all using data that are readily available to transit agencies. Conducting this analysis provides a foundation for beginning the planning process for transit priority infrastructure.

Transit Network Frequency Setting With Multi-Agent Simulation to Capture Activity-Based Mode Substitution

2021 ◽  
pp. 036119812110569
Author(s):  
Ziyi Ma ◽  
Joseph Y. J. Chow
Keyword(s):  
Large Scale ◽  
Operating Cost ◽  
Weighted Average ◽  
Average Error ◽  
Substitution Effects ◽  
Transit Network ◽  
Agent Simulation ◽  
Bus Network ◽  
Upper Level ◽  
Route Cost

We propose a bilevel transit network frequency setting problem in which the upper level consists of analytical route cost functions and the lower level is an activity-based market equilibrium derived using MATSim-NYC. The use of MATSim in the lower-level problem incorporates sensitivity of the design process to competition from other modes, including ride-hail, and can support large-scale optimization. The proposed method is applied to the existing Brooklyn bus network, which includes 78 bus routes, 650,000 passengers per day, 550 route-km, and 4,696 bus stops. MATSim-NYC modeling of the existing bus network has a ridership-weighted average error per route of 21%. The proposed algorithm is applied to a benchmark network and confirms their predicted 20% growth in ridership using their benchmark design. Applying our proposed algorithm to their network with 78 routes and 24 periods, we have a problem with 3,744 decision variables. The algorithm converged within 10 iterations to a delta of 0.064%. Compared with the existing scenario, we increased ridership by 20% and reduced operating cost by 25%. We improved the farebox recovery ratio from the existing 0.22 to 0.35, 0.06 more than the benchmark design. Analysis of mode substitution effects suggest that 2.5% of trips would be drawn from ride-hail while 74% would come from driving.

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