Parliamentary.ai


by Munro Research

Road Traffic (Congestion Reduction) Bill


Official Summary

A Bill to place a duty on highway authorities and police forces to minimise congestion and delays caused by collisions and other incidents on the highway; and for connected purposes

Summary powered by AnyModel

Overview

The Road Traffic (Congestion Reduction) Bill aims to reduce traffic congestion in England and Wales by placing a legal duty on local traffic authorities and police forces to minimize delays caused by road incidents. This involves quicker incident clearance, improved traffic management, and better contingency planning.

Description

This Bill amends existing legislation (Traffic Management Act 2004 and Police Act 1996). Key changes include:

  • Local Traffic Authorities: A stronger emphasis is placed on minimizing congestion in their network management duties. They must establish contingency plans for directing traffic around obstructions and publish annual reports on road closures, detailing their duration, cause, and actions taken.
  • Police Authorities: Their local policing plans must now include a requirement to minimize congestion during road incidents. Road closures should be a last resort, implemented for the shortest necessary time.

Government Spending

The bill doesn't directly allocate new funding. The anticipated impact on government spending is indirect, potentially involving increased costs associated with improved traffic management and incident response. However, no figures were provided in the presented bill text.

Groups Affected

  • Local Traffic Authorities: Increased responsibilities in managing congestion and reporting.
  • Police Forces: New requirements for minimizing congestion during incidents.
  • Road Users: Potential benefits of reduced congestion and quicker journey times.
  • Emergency Services: Potential impacts on their ability to respond to incidents, needing careful consideration to balance efficient emergency responses with congestion mitigation.
Full Text

Powered by nyModel

DISCLAIMER: AI technology is not 100% accurate and summaries may contain errors, use at your own risk. Munro Research holds the copyright for all summaries found this website. Reproduction for non-commercial purposes is permitted but must be displayed alongside a link to this website. Contact info@munro-research to license commercially.