traffic assignment problem equilibrium

Traffic Assignment: A Survey of Mathematical Models and Techniques

• First Online: 17 May 2018

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• Pushkin Kachroo 14 &
• Kaan M. A. Özbay 15  

Part of the book series: Advances in Industrial Control ((AIC))

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This chapter presents the fundamentals of the theory and techniques of traffic assignment problem. It first presents the steady-state traffic assignment problem formulation which is also called static assignment, followed by Dynamic Traffic Assignment (DTA), where the traffic demand on the network is time varying. The static assignment problem is shown in a mathematical programming setting for two different objectives to be satisfied. The first one where all users experience same travel times in alternate used routes is called user-equilibrium and another setting called system optimum in which the assignment attempts to minimize the total travel time. The alternate formulation uses variational inequality method which is also presented. Dynamic travel routing problem is also reviewed in the variational inequality setting. DTA problem is shown in discrete and continuous time in terms of lumped parameters as well as in a macroscopic setting, where partial differential equations are used for the link traffic dynamics. A Hamilton–Jacobi- based travel time dynamics model is also presented for the links and routes, which is integrated with the macroscopic traffic dynamics. Simulation-based DTA method is also very briefly reviewed. This chapter is taken from the following Springer publication and is reproduced here, with permission and with minor changes: Pushkin Kachroo, and Neveen Shlayan, “Dynamic traffic assignment: A survey of mathematical models and technique,” Advances in Dynamic Network Modeling in Complex Transportation Systems (Editor: Satish V. Ukkusuri and Kaan Özbay) Springer New York, 2013. 1-25.

This chapter is taken from the following Springer publication and is reproduced here, with permission and with minor changes: Pushkin Kachroo, and Neveen Shlayan, “Dynamic traffic assignment: A survey of mathematical models and techniques,” Advances in Dynamic Network Modeling in Complex Transportation Systems (Editor: Satish V. Ukkusuri and Kaan Özbay) Springer New York, 2013. 1–25.

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Kachroo, P., Özbay, K.M.A. (2018). Traffic Assignment: A Survey of Mathematical Models and Techniques. In: Feedback Control Theory for Dynamic Traffic Assignment. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-69231-9_2

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A Deep Learning Approach for the Traffic Assignment Problem

User Equilibrium (UE) is a classical and essential traffic assignment theory that has gained significant popularity over the past decades. While existing studies mainly focus on mathematical algorithms and their improvements, this paper proposes a novel method based on a deep learning approach, Long Short-Term Memory (LSTM) model, with aims to improve its efficiency while guaranteeing both precision and practicability. The new approach retains the initialization step of the Frank-Wolfe algorithm (all-or-nothing assignment based on free-flow travel time) and replaces its subsequent iterations with an LSTM prediction model. Furthermore, as the training process taking various characteristics like initial link flows, link capacities and free-flow travel time into consideration, a trained model is capable of handling different circumstances. Two numerical experiments based on the Sioux-Falls network and a large-scale Winnipeg network demonstrate its effectiveness and efficiency, with the average mean relative errors (MREs) below 5%. Since the initial link flows for input was assigned by randomly generated origin-destination (OD) demand, the proposed method can be easily applied to large-scale urban networks. In the later one-thousand nodes Winnipeg network, the Central Processing Unit (CPU) time required for the completion of the assignment diminishes from eighty minutes to only half an hour, by 62.5%.

• This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

Transportation Research Board

• Fang, Zhiyan
• Cheng, Qixiu
• Liu, Zhiyuan
• Transportation Research Board 98th Annual Meeting
• Location: Washington DC, United States
• Date: 2019-1-13 to 2019-1-17
• Media Type: Digital/other
• Features: References; Tables;
• Pagination: 6p

Subject/Index Terms

• TRT Terms: Machine learning ; Mathematical prediction ; Traffic assignment
• Uncontrolled Terms: Deep learning
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01697446
• Record Type: Publication
• Report/Paper Numbers: 19-01956
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:50PM

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This program solves the user equilibrium and stochastic user equilibrium for the city network

prameshk/Traffic-Assignment

Folders and files, repository files navigation, static-traffic-assignment.

Traffic-Assignment is a repository for static traffic assignment python code. Currently, the program can solve the static traffic assignment problem using user equilibrium (UE) and stochastic user equilibrium (SUE) for the city network. The solution can be achieved both using Method of Successive Averages (MSA) and Frank-Wolfe (F-W) algorithm.

Install dependencies

-heapq -numpy -scipy

How to run traffic assignment

Clone the repository on a local directory, data preparation :.

Navigate to the "network" folder (e.g., Sioux Falls network) and check the demand and network file format. For more network data, please refer to TNTP . Note that the data format used by the current script is different from the data available on this website. Use script "dataPreparation.py" to create a network suitable for this script.

Running the script :

Open the script "ta.py". Set the "inputLocation" to the directory where the network is stored. Use the following methods to perform operations:

Loading can be "deterministic" or "stochastic". The deterministic loading uses all or nothing assignment whereas stochastic loading uses Dial's algorithm to produce auxiliary flows.

algorithm can be "MSA" or "FW". MSA refers to method of successive averages and FW refers to Frank-Wolfe method to compute the step size.

accuracy is the tolerance parameter used to stop the algorithm when the solution is close to UE or SUE. The default value is set of 0.01 (i.e., 1%)

maxIter is the maximum number of iterations to stop the program if the program is not able to reach the equilibrium solution for a given accuracy. The default value of 10000.

• Use this method to write the UE results after the assignment is finished. You can open the output file in notepad or MS excel.

How to cite

If you are using this program for your research, please cite this code as below:

Feel free to send an email to [email protected] if you have questions or concerns.

Future releases

Future releases will have an implementation of other traffic assignment algorithms such as Gradient Projection, Origin-based assignment, and Algorithm B.

• Python 100.0%

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Traffic assignment: A survey of mathematical models and techniques

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This chapter presents the fundamentals of the theory and techniques of traffic assignment problem. It first presents the steady-state traffic assignment problem formulation which is also called static assignment, followed by Dynamic Traffic Assignment (DTA), where the traffic demand on the network is time varying. The static assignment problem is shown in a mathematical programming setting for two different objectives to be satisfied. The first one where all users experience same travel times in alternate used routes is called user-equilibrium and another setting called system optimum in which the assignment attempts to minimize the total travel time. The alternate formulation uses variational inequality method which is also presented. Dynamic travel routing problem is also reviewed in the variational inequality setting. DTA problem is shown in discrete and continuous time in terms of lumped parameters as well as in a macroscopic setting, where partial differential equations are used for the link traffic dynamics. A Hamilton–Jacobi- based travel time dynamics model is also presented for the links and routes, which is integrated with the macroscopic traffic dynamics. Simulation-based DTA method is also very briefly reviewed. This chapter is taken from the following Springer publication and is reproduced here, with permission and with minor changes: Pushkin Kachroo, and Neveen Shlayan, “Dynamic traffic assignment: A survey of mathematical models and technique,” Advances in Dynamic Network Modeling in Complex Transportation Systems (Editor: Satish V. Ukkusuri and Kaan Özbay) Springer New York, 2013. 1-25.

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• Industrial and Manufacturing Engineering

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• Mathematical models Engineering & Materials Science 100%
• Travel time Engineering & Materials Science 56%
• Time varying networks Engineering & Materials Science 25%
• Mathematical programming Engineering & Materials Science 21%
• User experience Engineering & Materials Science 18%
• Partial differential equations Engineering & Materials Science 18%
• Dynamic models Engineering & Materials Science 14%

T1 - Traffic assignment

T2 - A survey of mathematical models and techniques

AU - Kachroo, Pushkin

AU - Özbay, Kaan M.A.

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

N2 - This chapter presents the fundamentals of the theory and techniques of traffic assignment problem. It first presents the steady-state traffic assignment problem formulation which is also called static assignment, followed by Dynamic Traffic Assignment (DTA), where the traffic demand on the network is time varying. The static assignment problem is shown in a mathematical programming setting for two different objectives to be satisfied. The first one where all users experience same travel times in alternate used routes is called user-equilibrium and another setting called system optimum in which the assignment attempts to minimize the total travel time. The alternate formulation uses variational inequality method which is also presented. Dynamic travel routing problem is also reviewed in the variational inequality setting. DTA problem is shown in discrete and continuous time in terms of lumped parameters as well as in a macroscopic setting, where partial differential equations are used for the link traffic dynamics. A Hamilton–Jacobi- based travel time dynamics model is also presented for the links and routes, which is integrated with the macroscopic traffic dynamics. Simulation-based DTA method is also very briefly reviewed. This chapter is taken from the following Springer publication and is reproduced here, with permission and with minor changes: Pushkin Kachroo, and Neveen Shlayan, “Dynamic traffic assignment: A survey of mathematical models and technique,” Advances in Dynamic Network Modeling in Complex Transportation Systems (Editor: Satish V. Ukkusuri and Kaan Özbay) Springer New York, 2013. 1-25.

AB - This chapter presents the fundamentals of the theory and techniques of traffic assignment problem. It first presents the steady-state traffic assignment problem formulation which is also called static assignment, followed by Dynamic Traffic Assignment (DTA), where the traffic demand on the network is time varying. The static assignment problem is shown in a mathematical programming setting for two different objectives to be satisfied. The first one where all users experience same travel times in alternate used routes is called user-equilibrium and another setting called system optimum in which the assignment attempts to minimize the total travel time. The alternate formulation uses variational inequality method which is also presented. Dynamic travel routing problem is also reviewed in the variational inequality setting. DTA problem is shown in discrete and continuous time in terms of lumped parameters as well as in a macroscopic setting, where partial differential equations are used for the link traffic dynamics. A Hamilton–Jacobi- based travel time dynamics model is also presented for the links and routes, which is integrated with the macroscopic traffic dynamics. Simulation-based DTA method is also very briefly reviewed. This chapter is taken from the following Springer publication and is reproduced here, with permission and with minor changes: Pushkin Kachroo, and Neveen Shlayan, “Dynamic traffic assignment: A survey of mathematical models and technique,” Advances in Dynamic Network Modeling in Complex Transportation Systems (Editor: Satish V. Ukkusuri and Kaan Özbay) Springer New York, 2013. 1-25.

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Network assignment

What is Network Assignment?

Role of Network Assignment in Travel Forecasting

Overview of Methods for Traffic Assignment for Highways

All-or-nothing Assignments

Incremental assignment

Brief History of Traffic Equilibrium Concepts

Calculating Generalized Costs from Delays

Challenges for Highway Traffic Assignment

Transit Assignment

Latest Developments

Page categories

Topic Circles

Trip Based Models

More pages in this category:

# what is network assignment.

In the metropolitan transportation planning and analysis, the network assignment specifically involves estimating travelers’ route choice behavior when travel destinations and mode of travel are known. Origin-destination travel demand are assigned to a transportation network in order to estimate traffic flows and network travel conditions such as travel time. These estimated outputs from network assignment are compared against observed data such as traffic counts for model validation .

Network assignment is a mathematical problem which is solved by a solution algorithm through the use of computer. It is usually resolved as a travel cost optimization problem for each origin-destination pair on a model network. For every origin-destination pair, a path is selected that typically minimizes travel costs. The simplest kind of travel cost is travel time from beginning to end of the trip. A more complex form of travel cost, called generalized cost, may include combinations of other costs of travel such as toll cost and auto operating cost on highway networks. Transit networks may include within generalized cost weights to emphasize out-of-vehicle time and penalties to represent onerous tasks. Usually, monetary costs of travel, such as tolls and fares, are converted to time equivalent based on an estimated value of time. The shortest path is found using a path finding algorithm .

The surface transportation network can include the auto network, bus network, passenger rail network, bicycle network, pedestrian network, freight rail network, and truck network. Traditionally, passenger modes are handled separately from vehicular modes. For example, trucks and passenger cars may be assigned to the same network, but bus riders often are assigned to a separate transit network, even though buses travel over roads. Computing traffic volume on any of these networks first requires estimating network specific origin-destination demand. In metropolitan transportation planning practice in the United States, the most common network assignments employed are automobile, truck, bus, and passenger rail. Bicycle, pedestrian, and freight rail network assignments are not as frequently practiced.

# Role of Network Assignment in Travel Forecasting

The urban travel forecasting process is analyzed within the context of four decision choices:

• Personal Daily Activity
• Locations to Perform those Activities
• Mode of Travel to Activity Locations, and
• Travel Route to the Activity Locations.

Usually, these four decision choices are named as Trip Generation , Trip Distribution , Mode Choice , and Traffic Assignment. There are variations in techniques on how these travel decision choices are modeled both in practice and in research. Generalized cost, which is typically in units of time and is an output of the path-choice step of the network assignment process, is the single most important travel input to other travel decision choices, such as where to travel and by which mode. Thus, the whole urban travel forecasting process relies heavily on network assignment. Generalized cost is also a major factor in predicting socio-demographic and spatial changes. To ensure consistency in generalized cost between all travel model components in a congested network, travel cost may be fed back to the earlier steps in the model chain. Such feedback is considered “best practice” for urban regional models. Outputs from network assignment are also inputs for estimating mobile source emissions as part of a review of metropolitan area transportation plans, a requirement under the Clean Air Act Amendments of 1990 for areas not in attainment of the National Ambient Air Quality Standard.

# Overview of Methods for Traffic Assignment for Highways

This topic deals principally with an overview of static traffic assignment. The dynamic traffic assignment is discussed elsewhere.

There are a large number of traffic assignment methods, but they all have at their core a procedure called “all-or-nothing” (AON) traffic assignment. All-or-nothing traffic assignment places all trips between an origin and destination on the shortest path between that origin and destination and no trips on any other possible path (compare path finding algorithm for a step-by-step introduction). Shortest paths may be determined by a well-known algorithm by Dijkstra; however, when there are turn penalties in the network a different algorithm, called Vine building , must be used instead.

# All-or-nothing Assignments

The simplest assignment algorithm is the all-or-nothing traffic assignment. In this algorithm, flows from every origin to every destination are assigned using the path finding algorithm , and travel time remains unchanged regardless of travel volumes.

All-or-nothing traffic assignment may be used when delays are unimportant for a network. Another alternative to the user-equilibrium technique is the stochastic traffic assignment technique, which assumes variation in link level travel time.

One of the earliest, computationally efficient stochastic traffic assignment algorithms was developed by Robert Dial. [1] More recently the k-shortest paths algorithm has gained popularity.

The biggest disadvantage of the all-or-nothing assignment and the stochastic assignment is that congestion cannot be considered. In uncongested networks, these algorithms are very useful. In congested conditions, however, these algorithm miss that some travelers would change routes to avoid congestion.

# Incremental assignment

The incremental assignment method is the simplest way to (somewhat rudimentary) consider congestion. In this method, a certain share of all trips (such as half of all trips) is assigned to the network. Then, travel times are recalculated using a volume-delay function , or VDF. Next, a smaller share (such as 25% of all trips) is assigned based using the revised travel times. Using the demand of 50% + 25%, travel times are recalculated again. Next, another smaller share of trips (such as 10% of all trips) is assigned using the latest travel times.

A large benefit of the incremental assignment is model runtime. Usually, flows are assigned within 5 to 10 iterations. Most user-equilibrium assignment methods (see below) require dozens of iterations, which increases the runtime proportionally.

In the incremental assignment, the first share of trips is assigned based on free-flow conditions. Following iterations see some congestion, on only the very last trip to be assigned will consider true congestion levels. This is reasonable for lightly congested networks, as a large number of travelers could travel at free-flow speed.

The incremental assignment works unsatisfactorily in heavily congested networks, as even 50% of the travel demand may lead to congestion on selected roads. The incremental assignment will miss the fact that a portion of the 50% is likely to select different routes.

# Brief History of Traffic Equilibrium Concepts

Traffic assignment theory today largely traces its origins to a single principle of “user equilibrium” by Wardrop [2] in 1952. Wardrop’s “first” principle simply states (slightly paraphrased) that at equilibrium not a single driver may change paths without incurring a greater travel impedance . That is, any used path between an origin and destination must have a shortest travel time between the origin and destination, and all other paths must have a greater travel impedance. There may be multiple paths between an origin and destination with the same shortest travel impedance, and all of these paths may be used.

Prior to the early 1970’s there were many algorithms that attempted to solve for Wardrop’s user equilibrium on large networks. All of these algorithms failed because they either did not converge properly or they were too slow computationally. The first algorithm to be able to consistently find a correct user equilibrium on a large traffic network was conceived by a research group at Northwestern University (LeBlanc, Morlok and Pierskalla) in 1973. [3] This algorithm was called “Frank-Wolfe decomposition” after the name of a more general optimization technique that was adapted, and it found the minimum of an “objective function” that came directly from theory attributed to Beckmann from 1956. [4] The Frank-Wolfe decomposition formulation was extended to the combined distribution/assignment problem by Evans in 1974. [5]

A lack of extensibility of these algorithms to more realistic traffic assignments prompted model developers to seek more general methods of traffic assignment. A major development of the 1980s was a realization that user equilibrium traffic assignment is a “variational inequality” and not a minimization problem. [6] An algorithm called the method of successive averages (MSA) has become a popular replacement for Frank-Wolfe decomposition because of MSA’s ability to handle very complicated relations between speed and volume and to handle the combined distribution/mode-split/assignment problem. The convergence properties of MSA were proven for elementary traffic assignments by Powell and Sheffi and in 1982. [7] MSA is known to be slower on elementary traffic assignment problems than Frank-Wolfe decomposition, although MSA can solve a wider range of traffic assignment formulations allowing for greater realism.

A number of enhancements to the overall theme of Wardop’s first principle have been implemented in various software packages. These enhancements include: faster algorithms for elementary traffic assignments, stochastic multiple paths, OD table spatial disaggregation and multiple vehicle classes.

# Calculating Generalized Costs from Delays

Equilibrium traffic assignment needs a method (or series of methods) for calculating impedances (which is another term for generalized costs) on all links (and nodes) of the network, considering how those links (and nodes) were loaded with traffic. Elementary traffic assignments rely on volume-delay functions (VDFs), such as the well-known “BPR curve” (see NCHRP Report 365), [8] that expressed travel time as a function of link volume and link capacity. The 1985 US Highway Capacity Manual (and later editions through 2010) made it clear to transportation planners that delays on large portions of urban networks occur mainly at intersections, which are nodes on a network, and that the delay on any given intersection approach relates to what is happening on all other approaches. VDFs are not suitable for situations where there is conflicting and opposing traffic that affects delays. Software for implementing trip-based models are now incorporating more sophisticated delay relationships from the Highway Capacity Manual and other sources, although many MPO forecasting models still use VDFs, exclusively.

# Challenges for Highway Traffic Assignment

Numerous practical and theoretical inadequacies pertaining to Static User Equilibrium network assignment technique are reported in the literature. Among them, most widely noted concerns and challenges are:

• Inadequate network convergence;
• Continued use of legacy slow convergent network algorithm, despite availability of faster solution methods and computers;
• Non-unique route flows and link flows for multi-class assignments and for assignment on networks that include delays from opposing and conflicting traffic;
• Continued use of VDFs , when superior delay estimation techniques are available;
• Unlikeness of a steady-state network condition;
• Impractical assumption that all drivers have flawless route information and are acting without bias;
• Every driver travels at the same congested speed, no vehicle traveling on the same link overtakes another vehicle;
• Oncoming traffic does not affect traffic flows;
• Interruptions, such as accidents or inclement weather, are not represented;
• Traffic does not form queues;
• Continued use of multi-hour time periods, when finer temporal detail gives better estimates of delay and path choice.

# Transit Assignment

Most transit network assignment in implementation is allocation of known transit network specific demand based on routes, vehicle frequency, stop location, transfer point location and running times. Transit assignments are not equilibrium, but can be either all-or-nothing or stochastic. Algorithms often use complicated expressions of generalized cost which include the different effects of waiting time, transfer time, walking time (for both access and egress), riding time and fare structures. Estimated transit travel time is not directly dependent on transit passenger volume on routes and at stations (unlike estimated highway travel times, which are dependent on vehicular volumes on roads and at intersection). The possibility of many choices available to riders, such as modes of access to transit and overlaps in services between transit lines for a portion of trip segments, add further complexity to these problems.

# Latest Developments

With the increased emphasis on assessment of travel demand management strategies in the US, there have been some notable increases in the implementation of disaggregated modeling of individual travel demand behavior. Similar efforts to simulate travel route choice on dynamic transportation network have been proposed, primarily to support the much needed realistic representation of time and duration of roadway congestion. Successful examples of a shift in the network assignment paradigm to include dynamic traffic assignment on a larger network have emerged in practice. Dynamic traffic assignments are able to follow UE principles. An even newer topic is the incorporation of travel time reliability into path building.

Building smart transport in Moscow

Smart transport is foundational to any smart city; it is a system that wields a vast array of information and communication technologies to improve efficiency, convenience, and safety across a variety of vehicles and infrastructure assets. But it is a daunting undertaking for cities looking to digitize, with hundreds and thousands of citizens taking daily rides that must run smoothly, cleanly, and on time. In this Q&A, Moscow Deputy Mayor for Transport Maksim Liksutov discusses the city’s efforts to develop a smart transport system that Muscovites enjoy using and that anticipates their ever-changing needs.

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McKinsey: Describe Moscow’s transportation challenge and how the city has been addressing it.

Maksim Liksutov: Until 2010, the traffic situation in Moscow was close to critical: the road network had reached maximum capacity, and Moscow had one of the worst road traffic situations in the world. Thus, in 2011 the Government of Moscow and leading Russian and international experts developed the State Program of Moscow Transport Development to 2020. The plan centers on an analysis of large amounts of commuting data to reduce the load on the roads through a strategic approach to upgrades and new construction, as well as the launch of an intelligent transport system (ITS).

The ITS, which controls more than 2,000 video surveillance cameras, 3,700 road detectors, and 6,000 traffic lights, allows us to provide real-time response to traffic situations throughout the city rather than waiting for Muscovites to call emergency responders, law enforcement, or others to resolve issues. The mayor of Moscow was personally involved in developing and implementing the traffic-improvement measures that resulted in a significant reduction in congestion. Despite that, the number of registered private cars in Moscow increased by more than one million since 2010. In fact, according to the TomTom ranking, 1 1. “TomTom traffic index: Moscow,” TomTom, accessed December 4, 2017, www.tomtom.com/en_gb/trafficindex/city/moscow. Moscow was the most congested out of nearly 400 cities in 2010; by 2016, we had moved down to 13th. The traffic speed in Moscow increased by more than 13 percent—from 45 km/h in 2010 to 51 km/h in 2016. Such congestion reduction is among the best in the world.

We still experience congestion during peak traffic hours, but the improvement has been substantial thanks to the ITS, major changes in parking policies, and significant investments in public transportation, such as metro and buses. In 2017, Moscow won the TomTom award for parking, ranking first globally in quality of parking planning.

McKinsey: What steps is Moscow taking to increase use of the public transport system?

Maksim Liksutov: It is difficult for public transport to compete with the comfort of the car, so we set out to ensure public transport is safe, modern, reliable, accessible, and accommodates the needs of each passenger.

First of all, we have been upgrading our vehicle fleet. Since 2010, we have purchased more than 8,000 new ground transportation vehicles and 1,600 new metro train cars, all manufactured domestically. By the end of 2017, the share of new train cars being used on the metro will reach 37 percent, and ground transportation vehicles will be at 90 percent. Today, the average age of urban buses is less than five years, and 98 percent of our ground transportation vehicles are accessible to disabled passengers. The Moscow metro offers a special assistance service, and there are also “social taxis” to help the elderly and the disabled navigate the city.

Second, we have implemented several modern services found in the best transportation systems in the world, including electronic ticketing systems, a city bicycle system, bus lanes, and a regulated taxi industry. Today, more than 85 percent of trips on public transport are paid for with Troika transport cards, which were introduced in 2013 and enable seamless transfers between all types of surface transport. In 2017, Muscovites made 2.3 million city bicycle trips, twice as many as in 2015. Bus lanes ensure that public transport vehicles are given priority in traffic, which has improved the regularity of bus service in central Moscow; annual full-fare trips on surface transit increased from 586 million in 2010 to one billion in 2017. And thanks to high competition and legalization of the market, Moscow’s 47,000 legal taxis have seen a 16-fold increase in ridership since 2010.

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Third, we have been focused on using data to improve the passenger experience and inform our public transport investments. In addition to helping streamline private car usage and traffic conditions, we use the ITS to collect an extensive data set on bus passenger boarding and alighting, frequency and speed, and load on roads and hubs. Mobile data and tracking systems give us an accurate picture of each bus’s movement. This informed our launch of a new bus route network called Magistral, which has given the more than 900,000 people working downtown access to an efficient alternative to metro travel that would require line changes.

Fourth, we sought to improve connectivity between city districts and relieve the load on metro and train stations by building the Moscow Central Ring, which encircles the city center and connects all of our metro rail lines. Within one year of operation, passenger traffic on this circular railway reached 400,000 trips per day.

Finally, we recently introduced a smart closed-circuit television (CCTV) system to ensure passenger safety. The CCTV automatically records and detects potentially dangerous situations, from unusual crowds to lost or abandoned items, and can even recognize faces. We anticipate that the new security system will provide a tenfold improvement in emergency response times for Moscow metro employees.

As a result of these efforts, Muscovites are making the public system their main mode of transport; the number of full-fare trips taken annually increased from 1.9 billion in 2010 to 2.8 billion in 2017. And today, intervals between trains during peak hours on the busiest lines are at 90 seconds, which keeps the system running smoothly. This indicator is a record among the world’s major underground systems. 2 2. Alexey Timofeychev, “18 little known facts about the Moscow Metro,” Russia Beyond the Headlines, January 19, 2016, rbth.com. Indeed, according to a 2016 study by Community of Metros (CoMET), an independent international association, Moscow has one of the world’s top three metro systems for passenger satisfaction with real-time information.

McKinsey: How do you collect passengers’ feedback on their experiences of using transport, and how do you use this feedback to help make decisions?

Maksim Liksutov: No initiative is implemented without considering the views of Muscovites. Moscow has two service centers that receive questions, suggestions, requests, and appeals from more than 5,000 people every week through telephone, internet, or personal contact. We also process all inquiries and suggestions submitted through social media networks.

Our latest tools for interaction with citizens are city transportation network mobile applications, which Muscovites have downloaded 3.5 million times. The apps can be used to plan a trip using public transportation, pay for parking, and find the nearest bike rental station. The “Moscow Assistant” app even allows residents to register parking violations. About 200,000 residents use the app, and more than 230,000 fines were created in 2017.

At the same time, we are constantly improving our data handling with the goal of anticipating the wishes of Muscovites. We use the same advanced analytics and data processing methods as mobile operators and leading internet services. But unlike these groups, we work with a large volume of diverse data that come from metro and bus trips, photo and video recordings of violations, vehicle tracking, tracking of mobile applications, and Wi-Fi use. With this data in hand, we process feedback from passengers and provide relevant and up-to-date information on city events. We can also change the route network, for example, if we see there is a new hub of activity in the city that needs public transport service. We have just started to develop the mechanism, and much remains to be done in this regard.

McKinsey: What are the main problems that arise in the process of smart-city management and use of big data?

Maksim Liksutov: Data protection is a primary concern in the management of any smart city. The introduction of smart technologies involves many risks, and we want to provide the most reliable protection available. This month, the Moscow Center for Traffic Management set up a new protective barrier for the virtual infrastructure of the ITS, including a set of advanced software protection measures that ensure full security. Now our ITS is defended by modern, cyber software and endpoint protection. The solution minimizes the risk of malicious software penetrating the city’s databases and protects against leaks of confidential information and personal data.

McKinsey: What is your vision of Moscow in 2025? How does it fit into the global landscape of smart transport technology?

Maksim Liksutov: To start, we are committed to continuing to increase the convenience of ticketing and payment methods for public transport, exploring methods such as wearable ticketing technology.

In the realm of personalized travel, we recently began testing a new method of pushing information to metro passengers. Given the data obtained from Troika cards, we can recommend to each individual passenger the most convenient ways to use the city’s public transport system (custom-made transport). We hope that in future, personalized information provision will become a convenient tool for managing passenger traffic, and the opportunities of big data will contribute to comfort and safety of Muscovites.

We understand that public transport plays an important role in reducing air pollution and creating a healthy city. As such, in the coming years Moscow intends to become the world leader in the development of electric public transport. With the city’s buses carrying millions of people a day, procurement of an ecofriendly and comfortable fleet is a top priority. We will phase in electric buses over the next few years, and in 2021, Moscow will stop purchasing diesel buses, opting instead for an entirely electric fleet.

And of course, we will continue to create a more convenient route network that stays ahead of Muscovites’ needs by providing buses and adding routes and stops based on what users say and technologies reveal are most needed.

McKinsey: What advice do you have for other city leaders?

Maksim Liksutov: I am not in a position to give it; every city, especially a megacity, is unique. Since we started reforming our transport system later than most world capitals, Moscow has had the opportunity to learn from and apply the experience of cities such as Beijing, London, Singapore, and Tokyo. We are working to implement the best solutions available across the world—and I know other city leaders are working to learn from one another and do the same for their home town.

Photo courtesy of the City of Moscow

Maksim Liksutov is the deputy mayor for transport in Moscow.

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Moscow, like other international urban areas , is decentralizing, despite considerable barriers. The expansion will lead to even more decentralization, which is likely to lead to less time "stuck in traffic" and more comfortable lifestyles. Let's hope that Russia's urban development policies, along with its plans to restore population growth, will lead to higher household incomes and much improved economic performance.

Wendell Cox is a Visiting Professor, Conservatoire National des Arts et Metiers, Paris and the author of “ War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life ”

Note 1: The 23 ward (ku) area of Tokyo is the geography of the former city of Tokyo, which was abolished in the 1940s. There is considerable confusion about the geography of Tokyo. For example, the 23 ward area is a part of the prefecture of Tokyo, which is also called the Tokyo Metropolis, which has led some analysts to think of it as the Tokyo metropolitan area (labor market area). In fact, the Tokyo metropolitan area, variously defined, includes, at a minimum the prefectures of Tokyo, Kanagawa, Chiba and Saitama with some municipalities in Gunma, Ibaraki and Tochigi. The metropolitan area contains nearly three times the population of the "Tokyo Metropolis."

Note 2: The expansion area (556 square miles or 1,440 square kilometers) has a current population of 250,000.

Note 3: Includes all residents in suburban districts with at least part of their population in the urban area.

Note 4: Urban area data not yet available.

Photo: St. Basil's Cathedral (all photos by author)

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Russians seeing the light while Western elites are bickering?

What an extremely interesting analysis - well done, Wendell.

It is also extremely interesting that the Russian leadership is reasonably pragmatic about urban form, in contrast to the "planners" of the post-rational West.

An acquaintance recently sent me an article from "The New Yorker", re Moscow's traffic problems.

The article "abstract" is HERE (but access to the full article requires subscription)

http://www.newyorker.com/reporting/2010/08/02/100802fa_fact_gessen

One classic quote worth taking from it, is: "People will endure all manner of humiliation to keep driving".

I do find it odd that the "New Yorker" article author says nothing at all about the rail transit system Moscow had, on which everyone was obliged to travel, under Communism. It can't surely have vaporised into thin air?

Moscow is a classic illustration of just how outmoded rails are, and how important "automobility" is, when the auto supplants rails so rapidly than even when everybody did travel on rails up to a certain date, and the road network dates to that era, when nobody was allowed to own a car; an article written just 2 decades later does not even mention the rail transit system, other than to criticise the mayor for "failing to invest in a transit system".......!!!!!!!!

This is also a give-away of "The New Yorker's" inability to shake off the modern PC ideology on rails vs cars.

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