Soft measures
Soft measures are non-structural interventions that reduce climate impacts without necessarily modifying the physical hazard itself.
Nothing is built. What changes is who travels, when, how fast, and on what information.
What they look like
Within the transport context, soft measures cover a wide range. The handbook groups them loosely; they sort into four kinds of action.
Travel-demand management and behavioural adaptation
Reducing, rescheduling or avoiding trips during hazardous conditions.
This is the same behavioural response modelled in social behavioural impacts, applied deliberately as a policy rather than observed as a reaction.
Warning and forecasting
Early warning and forecasting feeding operational decisions:
- Heat-health alerts
- Flood warnings
Operational planning and protocols
| Measure | Applies to |
|---|---|
| Heat action plans and hot-weather timetables | Planned service adjustment during heat |
| Precautionary speed restrictions on rail during buckling risk | Rail operations |
| Operational thresholds and closure protocols | Deciding when to restrict or close |
Operational thresholds appear in two chapters
These are the same operational thresholds catalogued in transport thresholds.
There they are documented as evidence of when disruption occurs. Here they are treated as something that can be changed, which makes protocol design itself an adaptation option.
Maintenance regimes
Such as drainage-gully clearance.
The cheapest measure in the chapter
Gully clearance is maintenance, not construction, and it restores designed drainage capacity that has been lost to silt and debris.
Its inclusion alongside barriers and SuDS is a reminder that some resilience is recovered rather than built.
Enabling technologies
Some of these measures can be enabled or facilitated by emerging technologies:
| Technology | Enables |
|---|---|
| Remote sensing | Wide-area monitoring of conditions |
| Asset sensor networks | Condition data from the infrastructure itself |
| Real-time hazard forecasting | Warnings issued early enough to act on |
| Automated information and routing systems | Getting guidance to travellers in time to change their journey |
The last row is what converts a warning into an avoided trip. A flood alert nobody receives changes no behaviour.
How they are modelled
Soft measures are implemented within the transport model, through modification of travel demand.
This allows the effects of reduced or altered travel during flood events to be evaluated directly.
The hazard is unchanged
Unlike green and grey measures, soft measures leave the flood map exactly as it was. The same water reaches the same links at the same depth.
What changes is how many people are on those links when it does, which is why the benefit appears in operational and social metrics rather than physical ones.
Required input data
| Input | Why it is needed |
|---|---|
| Baseline travel demand | The unadapted pattern the measure is a change from |
| Spatial and temporal distribution of trips | Where and when the demand sits, so it can be selectively reduced |
| Assumed level of demand reduction or modification | The strength of the intervention being tested |
The third input is an assumption, not a measurement
The handbook is explicit that the level of demand reduction is assumed.
How many people actually change their travel in response to a warning is genuinely uncertain, and the modelled benefit of a soft measure scales directly with that assumption.
Results should therefore be reported alongside the assumed response rate, and ideally tested across a range of it, rather than presented as a single figure.
Generated output data
The transport model represents the specified modification in travel demand. The adapted scenarios can then be simulated to quantify:
| Output | Corresponding metrics |
|---|---|
| Changes in travel time and delay | OP1 to OP4 |
| Disrupted or cancelled journeys | OP5 and OP6 |
| Network performance | Across the POESE set |
| Associated economic impacts | EC1 to EC10 |
These outputs enable comparison between baseline and adaptation scenarios, supporting assessment of adaptation effectiveness and subsequent cost-benefit analysis.