Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.

A practical guide to the American Community Survey (3-year estimates)

Transportation research. part c, emerging technologies

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

This paper develops a novel linear programming formulation for autonomous intersection control (LPAIC) accounting for traffic dynamics within a connected vehicle environment. Firstly, a lane based bi-level optimization model is introduced to propagate traffic flows in the network, accounting for dynamic departure time, dynamic route choice, and autonomous intersection control in the context of system optimum network model. Then the bi-level optimization model is transformed to the linear programming formulation by relaxing the nonlinear constraints with a set of linear inequalities. One special feature of the LPAIC formulation is that the entries of the constraint matrix has only {−1, 0, 1} values. Moreover, it is proved that the constraint matrix is totally unimodular, the optimal solution exists and contains only integer values. It is also shown that the traffic flows from different lanes pass through the conflict points of the intersection safely and there are no holding flows in the solution. Three numerical case studies are conducted to demonstrate the properties and effectiveness of the LPAIC formulation to solve autonomous intersection control.

A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment

Driving cycles are used to understand the driving pattern of vehicles and in estimating their emissions. Although several studies exist on driving cycles worldwide, few studies have focused on developing driving cycles for intra-city buses in heterogeneous traffic conditions. In this study, driving cycles for intra-city buses were developed using real-world GPS data collected during peak and off-peak periods in Chennai city, India. The methodology for the construction of the candidate cycle was based on k-means clustering and one-step Markov modelling. For Markov chain modelling, a transition matrix is constructed which is a probability matrix based on one step succession. To understand the effect of duration of the driving cycle, the candidate cycles were developed for different durations ranging from 400 seconds to 2800 seconds. Further, the average error for each duration of the candidate cycles was determined. The duration which corresponds to the least average error was chosen for developing the final driving cycle. Three driving cycles - corresponding to morning peak hour, off-peak hour, and evening peak hour - were developed. Finally, the developed cycles were compared with the existing local and international cycles. The developed driving cycle was found to be significantly different from the existing cycles for buses.

Developing driving cycles using k-means clustering and determining their optimal duration

We present a complex weighted network analysis of a bus transport network. We model the network as graphs in L-space and P-space and evaluate the statistical properties in unweighted and weighted cases. The weights considered include number of overlapping routes and passenger demand between two bus stops. We also introduce a new supply-based edge weight called Service Utilization Factor ( S U F ) and define it as the passenger demand per service between two stops. We extract the origin and destination of the passenger trips from bus ticket information. In the bus system under study, the tickets are issued between ’stages’ instead of between bus stops. We propose a points of interest based procedure to map the passenger demand between stages to between bus stops. The network has scale-free behaviour with preferential attachment of nodes and is similar to small-world networks. It is well-connected with an average of 1.4 transfers required to travel between any two points in the network. We identify redundant routes in the network from strength values in the SUF weighted networks. Disassortativity in the demand weighted network indicated that bus stops with higher attractiveness are not necessarily situated contiguously along a route. The SUF weighted network is assortative indicating presence of well-serviced stops along the same the route. We identified the stops with higher betweenness centrality, that can be ideal candidates for being developed as hubs. Our conclusions exemplify the importance of considering route overlaps, passenger demand and service utilization in bus network analysis and for building an efficient bus network.

Topological properties of bus transit networks considering demand and service utilization weight measures

In this paper, we study the structural properties of the complex bus network of Chennai. We formulate this extensive network structure by identifying each bus stop as a node, and a bus which stops at any two adjacent bus stops as an edge connecting the nodes. Rigorous statistical analysis of this data shows that the Chennai bus network displays small-world properties and a scale-free degree distribution with the power-law exponent, = 3:8. I. INTRODUCTION A. Chennai Bus Network Chennai is one of the metropolitan cities in India with a structured and a close-knit bus transport network. The Chennai bus network (CBN) is operated by the Metropolitan Transport Corporation (MTC), a state government undertaking. Spanning an area of 3,929 sq. km and with over 800 routes sprawling across entire Chennai, this extensive network also boasts of the largest bus terminus in Asia. With the population of the city being the sixth largest in the country, this medium of transport is most widely used for day-to-day commutation. The reason that the bus network, in general, achieves this favourable status lies primarily in two factors: (i) cost, and (ii) penetration. With high volume of commuters flowing through this network each day, the cost of travel per day, per person becomes significantly low. Although, this is a parameter which is usually met by every other public transport system, the penetration factor is the one which significantly makes bus networks the prime mode of transportation in cities. A high penetration effect exhibited by a bus network enables a person to travel between any two points in the city very easily which is in contrast to other public modes of transportation, where geographical constraints associated with the network layout are much higher in number. Thus, bus transportation allows flexibility in travel. A natural question arises here; what is the least number of buses one should change to go from one place to another? Or more precisely, why do we require changing buses at all? To answer these questions, we need to look at a more abstract and networked structure of the bus routes, and argue based on its various statistical properties. B. Complex Networks: overview

Scaling Laws in Chennai Bus Network

In this study, researchers have explored real-world driving conditions and developed emission factors for 58 passenger cars using on-board emission measurement technique while driving on five different routes in Delhi. The measured average emission factors of CO, HC, and NO were 3.99, 0.34, and 0.54 g/km for diesel vehicles, 7.26, 0.17, and 0.62 for petrol vehicles respectively. Road, traffic, vehicle type, and driving characteristics affect the quantity of emissions released. However, speed and acceleration significantly impact emission rates increasing with the increase in speed and acceleration. Also, emissions were minimal at 40–60 kmph and −0.5–0.5 m/s2. The estimated city-wide CO, HC, and NO emissions were 60.8, 4.8, and 9.72tonnes/day. These results demonstrate the importance of monitoring the real-world exhaust emissions given the substantial difference between test cycle measurements used for compliance testing of new vehicles.

Characteristics of real-world gaseous exhaust emissions from cars in heterogeneous traffic conditions

Intra-city buses provide essential transportation and mobility services in cities. However, most of the buses in Indian cities run on diesel fuel causing significant emissions and air quality issues. This study aims to quantify the emissions from the buses during peak and off-peak periods using second-by-second activity data collected using global positioning system (GPS) receivers. Four-bus routes plying within the city of Chennai in India was selected and second-by-second speed and acceleration was used to determine the operating mode of the bus. Corresponding emissions were estimated using vehicle specific power (VSP). Results show that the average speed of the bus during peak and off-peak periods were 17.8 kmph and 21.5 kmph, respectively; the corresponding percentages of time idling were 27% and 22%. Further, the percentage increase in total emissions of CO2, CO, HC and NOx from the bus during peak periods with respect to off-peak periods was 17%, 16%, 37% and 21%, respectively. This study provides useful insights regarding the operating and emission characteristics of buses that will be valuable to policy makers in improving their service and efficiency.

Gitakrishnan Ramadurai

Papers

Developing driving cycles using k-means clustering and determining their optimal duration

Topological properties of bus transit networks considering demand and service utilization weight measures

Scaling Laws in Chennai Bus Network

Characteristics of real-world gaseous exhaust emissions from cars in heterogeneous traffic conditions

Analysis of driving characteristics and estimation of pollutant emissions from intra-city buses