High congestion and pollution costs in urban areas across the globe.
Today, 10 million trips occur in urban areas every day. Traffic congestion is an issue for large agglomerations around the world. Urban road congestion arises mainly during peak commuting hours. As economic activities are spatially concentrated due to agglomeration economies, workers gather in cities close to their workplace. These economies depend on the traditional trade-off between scale economies and transportation costs (Brueckner, 2011). Congestion costs matter as households and firms waste time and fuel. Firms lose both productivity and efficiency based on their location and their business sector, whether their workers arrive late for work. Furthermore, logistics businesses suffer from delivery delays due to extra travel time within a city.
Congestion costs. The economic costs are relatively high. Vehicle owners have direct costs related to purchase, maintenance, value depreciation, fuel consumption and parking. In addition, there are indirect costs, such as congestion costs, which are measured as wasted time and fuel in traffic jams. In 2001, Americans spent 161 minutes on average per day in a private vehicle (Duranton and Turner, 2011). There was a decline in travel time due to the economic crisis from 2011 to 2013 (CEBR, 2014). However, economic activity recovered. Today, American cities remain among the most congested in the world. For instance, a commuter living in Los Angeles spends 102 minutes a day in rush hour traffic (Cookson, 2018). In France, 37.2 million vehicles were in circulation according to the survey “Transport et d´eplacements 2008” (CGDD, 2010). Daily travel time has reached 56.4 minutes in France in 2008 (CGDD, 2010). The distance traveled by each French person (active and mobile) grew by 8 kilometers between 1982 and 2008, which is an increase of 45% in 26 years. A densely populated area such as Paris saw home-to-work travel times hit 75 minutes on average in 2008, despite the fact that traveled distances are shorter than they were in 1982. Paris’ commuters wasted 55 hours on average in traffic for home-to-work trips in 2013 (CEBR, 2014). We can observe the discrepancy between travel times for work-related trips during peak and off-peak hours for the city of Lyon in 2014. Travel times increase with the distance from the CBD. It is straightforward to area with respect to the distance from Central Business Districts (CBD) in 2014. check that peak hour travel times are higher than off-peak. A driver spends 26 minutes for an average round trip in off-peak hours, while it takes 32.4 minutes during peak hours. That is a 25% increase. In addition, traffic congestion induces carbon dioxide emissions stemming from idling vehicles.
Policies aiming to lower GHG emissions and congestion in urban areas.
Urban policies may efficiently curb GHG emissions by mobilizing numerous actors such as local, regional and national public authorities (OECD, 2014a). Development of compact cities providing efficient public transport service and improved infrastructure related to soft (i.e., non-motorized) modes of transport is promoted by the IPCC. Indeed, the higher the share is of transport modes that use less carbon for trips involving people and goods, the lower the CO2 emissions per capita (Bongardt et al., 2013). A mix of land policies and urban planning oriented toward a limited space for private cars and a proximity of residential and business locations enable a city’s carbon footprint to be reduced (Suzuki et al., 2013). Policies of congestion management through tolls arise only in a few cities worldwide (London, Singapore, Stockholm, Trondheim, Oslo, etc.) as pointed out by Fosgerau and De Palma (2013). Political concerns and electoral cost of unpopular initiatives drive this lack of tax implementation (Fosgerau and De Palma, 2013).
Impact of anti-congestion policies on urban forms and environment.
In discussing the stakes of the new economic geography (NEG), Fujita and Krugman (2004) agreed that transportation costs are substantial to explain location decision of firms and households. Raising urban costs to internalize externalities of congestion may improve the allocation of resources but also have an impact on location decisions in the long run.
First, economists have assessed short-term consequences of implementing an urban toll, economists have focused on traffic, welfare effects, modal choice conditions and pricing equity (Eliasson and Mattson, 2006; Raux and Souche, 2004). They have also studied long-term effects regarding transport preferences and GHG emissions (Bhatt, 2011). Short-term consequences. In the seminal work of Solow (1972), the author determines a rent profile that is more convex in the case in which congestion is taken into account.
This means that rents become more expensive close to the CBD and relatively less at the edge of the city. Accordingly, households locate in smaller housing spaces near their workplace than at points farther away. Solow obtains a similar result in his subsequent paper in which he slightly changes the model (Solow, 1973). When congestion costs are introduced, land rents rise at all locations for a given city size. However, when housing density adjusts the city size expands. In the monocentric model, seven cases are analyzed where the pure distance costs and the congestion costs vary in the annual transportation cost function with the help of numerical simulations. The fraction of income spent on housing space after travel costs and the fraction of land allotted to residential use shift as well. When congestion costs are high, the diameter of the monocentric circular city increases gently. Some simulations are performed in which the fraction of land allotted to housing is low and increases from the CBD to the periphery. The simulations yield a flattening effect of the rent profile and a sprawling effect of the city boundary (Solow, 1972).
Solow (1972) is also concerned with the optimal fraction of land that can be allotted to the road system. In his seminal paper, he demonstrates that the best share between roads and housing is a fraction of land allocated to residential use yielding a minimum to the rent at the edge of the CBD. But his answer does not lead to an analytical solution, and he therefore leaves it for further research. Accordingly, in a subsequent study he carries out a cost-benefit analysis comparing shifts in the fraction of land use with the help of numerical simulations (Solow, 1973). Since no congestion tolls are implemented, land values represent diverse private transport costs. Households face a unique trade-off between rent and travel costs and do not take into account the social cost imposed to other families.
Numerical simulations are performed to compare three distinct planning decisions regarding the fraction of land devoted to housing and its effects on average annual rent, travel costs and welfare per household. The cost-benefit analysis leads to roads’ overcapacity within the city, especially close to CBD since market land values are distorted through the lack of a road-pricing scheme (Solow, 1973).
Regarding land use regulation in an urban model, a city planner is commonly used as a municipality for Solow (1972) or a manager for Arnott (1979). The former must choose the best allocation of land between roads and housing, and the latter must minimize its variable resource costs to supply the whole population in lot size, transportation and other goods consumed by each resident. Arnott (1979) demonstrates that transport improvement such as a road widening induces indirect costs that should be considered even though Solow (1973) ignored them. Indeed, it causes larger traffic flows than what existed previously and avoids an over-evaluation of land for both road and residential use. Transport upgrades in an unpriced road-scheme create a distorted urban economy in a long-run competitive equilibrium. Accordingly, indirect costs must be taken into account carefully in the cost-benefit analysis as it relates to optimal capacity allocated to roads.
Commuting and urban forms: case study of French municipality areas.
Finally, in this third chapter, we have collected data from the French national census to analyze the effects of urban forms on home-to-work distances and travel time during peak hours. Three periods are studied: 1999, 2007 and 2014. We use a distance meter called ”Odomatrix” which is a tool that measures road accessibility on the French network. Municipal data are combined with a road database from the National Institute for Geographic and Forestry Information (IGN). First, we define four measures of urban form. Our main concern is the spatial organization of workplaces and population within the French urban areas. In a second step, we specify an econometric model in order to evaluate the relevance and the magnitude of the relationship between these urban form measures and home-to-work distances over three periods. Third, we focus on the impacts of urban forms on peak hour travel times and distances between dwellings and workplaces only in 2014. Finally, we conduct an analysis of the relationship between urban form measures at the municipal level and commuting times and distances in 2014.
Table of contents :
1.1 High congestion and pollution costs in urban areas across the globe.
1.2 Policies aiming to lower GHG emissions and congestion in urban areas.
1.3 Economic and environmental implications of different urban spatial structures
2 Literature review
2.1 Modeling the polycentric city
2.2 Impact of anti-congestion policies on urban forms and environment.
3.1 Chapter 1: Urban spatial structure, transport-related emissions and welfare
3.2 Chapter 2: Efficiency of road pricing schemes with endogenous workplaces in polycentric city
3.3 Chapter 3: Commuting and urban forms: case study of French municipality areas
1 Urban spatial structure, transport-related emissions and welfare.
2 A simple model
3 The monocentric city
4 The polycentric city
5.1 Extending the city vertically
5.2 Endogenous wage
5.3 Role of modal choice and congestion
2 Efficiency of road pricing schemes with endogenous workplaces in polycentric city.
2 The model
2.1 The city
2.3 Congestion costs and transport infrastructure
2.4 Urban toll
3 The monocentric city
4 Decentralization of jobs and welfare
4.1 The polycentric city
4.2 Equilibrium allocation and optimal location of jobs
5 Polycentric city and road pricing schemes
6 Comparisons between road pricing schemes
6.1 Efficiency of the three road pricing schemes in the polycentric city .
7.1 Incidence of modal choice on congestion and urban structure .
3 Mobilit´es pendulaires et formes urbaines: cas des aires urbaines fran¸caises m´etropolitaines.
2 Donn´ees sur les d´eplacements domicile-travail et les aires urbaines
2.1 D´eplacements domicile-travail
2.2 La mesure des formes urbaines
2.3 Les variables de contrˆole
3 ´Evolution des distances moyennes domicile-travail parcourues et formes urbaines
4 Temps de trajet `a l’heure de pointe et formes urbaines en 2014
4.1 Influence de l’aire urbaine
4.2 Niveau communal
List of tables
List of figures